Abstract: A system, method, and computer program product provides a way to separate connected or adhered adjacent characters of a digital image for license plate recognition. As a threshold processing, the method performs a recognition of character adhesion by obtaining character parameters using an image processor. The parameters include a horizontal max crossing and a ratio of width and height. A first rule-based module is used responsive to the character parameters to distinguish the adhered characters (character adhesions) that are easy to judge, leaving the uncertain part to a character adhesion classifier model for discrimination. Character adhesion data is obtained by data augmentation including the adding of a random distance between two single characters to create class like adhered characters. Then the character adhesion classifier model of single character and character adhesion data is trained. Any uncertain part can be distinguished by the trained character adhesion classifier model.

Abstract: The present disclosure relates to systems and methods for image sharpening. The systems and methods may obtain a target image to be processed, the target image including one or more target pixels to be processed. For each of the one or more target pixels, the systems and methods may select one or more previous pixels and one or more subsequent pixels along a predetermined direction in the target image; determine a first gray value based on the one or more previous pixels and a second gray value based on the one or more subsequent pixels; select a target gray value from the first gray value and the second gray value based on an initial gray value of the target pixel; and determine an adjusted gray value of the target pixel based on the initial gray value and the target gray value.

Abstract: A boundary map and a first image are received. The boundary map is used to determine that brightness values for a set of pixels included in the first image should be regularized. The first set of pixels include, at a first pixel position, a first pixel having a first set of brightness values. The first set of pixels further includes, at a second pixel position, a second set of brightness values. An output image is generated by storing, at both a first and second pixel position in the output image, a set of regularized values.

Abstract: Provided herein are methods, systems, devices, and computer-readable storage media for selecting a detection area for a well (e.g., as part of an assay plate) comprising a plurality of encoded microcarriers. In some aspects, selecting the detection area includes obtaining one or more images of the well; calculating a center of the well according to a two-dimensional coordinate system; assigning a position, according to the two-dimensional coordinate system, of a first encoded microcarrier from the plurality; determining whether a distance between the position of the first encoded microcarrier and the center of the well according to the two-dimensional coordinate system exceeds a threshold distance; and including in the detection area one or more microcarriers whose distance from the center of the well does not exceed the threshold distance, while excluding those whose distance from the center of the well exceeds the threshold.

Abstract: Systems and methods are described for removing ringing artifacts from a coded image. For each block of coded image data, a direction is identified by selecting a directional block divided into a plurality of pixel lines in one predetermined direction of a set of at least four predetermined directions. The directional block may have a constant value across each pixel line. A parameter related to a sum of a mean-square difference between a pixel value and the pixel average of pixels falling on a pixel line in the directional block that includes the location of the pixel may be calculated for each direction of the set. The direction of the set having a minimum summed mean-square difference is selected as the direction for the block based on the calculated parameter values. A non-linear filter is then applied to each block based on the identified direction for the block.

Abstract: Systems and methods for determining optimized imaging parameters for imaging a patient include learning a model of a relationship between known imaging parameters and a quality measure, the known imaging parameters and the quality measure being determined from training data. Optimized imaging parameters are determined by optimizing the quality measure using the learned model. Images of the patient are acquired using the optimized imaging parameters.

Abstract: A method is disclosed for efficiently calculating a median value of a high-order array in a Single Instruction Multiple Data (SIMD) processor. Values of the high-order array are sorted vertically in each column followed by sorts on each individual row. After the sort, selective diagonal values of the sorted high-order array are used to form a low-order array to calculate the median of the high-order array. The median calculation using selective diagonal values of the high-order array in a low-order array significantly improves SIMD processor efficiency and throughput.

Abstract: In one embodiment, as new blocks of data are written to storage devices of a storage system, fingerprints are generated for those new blocks and inserted as entries into a top level (L0) of a dense tree data structure. When L0 is filled, the contents from L0 may be merged with level 1 (L1). After the initial merge, new fingerprints are added to L0 until L0 fills up again, which triggers a new merge. Duplicate fingerprints in L0 and L1 are identified which, in turn, indicates duplicate data blocks. A post-processing deduplication operation is then performed to remove duplicate data blocks corresponding to the duplicate fingerprints. In a different embodiment, as new fingerprint entries are loaded into L0, those new fingerprints may be compared with existing fingerprints loaded into L0 and/or other levels to facilitate inline deduplication to identify duplicate fingerprints and subsequently perform the deduplication operation.

Abstract: A method for determining a median value of an array of pixels in a vision system may be performed in an efficient manner using the parallel computing capabilities of a SIMD processing engine. Each column of an array may be sorted in ascending (descending) order to form a first sorted array. Each row of the first sorted array may be sorted in ascending (descending) order to form a second sorted array. A pixel may be selected as the median value from a diagonal portion of the second sorted array, wherein the diagonal portion bisects a lower value region and a higher value region of the second sorted array.

Abstract: An image processing apparatus includes: a processor; and memory storing computer readable instructions, when executed by the processor, causing the image processing apparatus to execute: acquiring target data representing a target image; calculating, for each of a plurality of target pixels forming the target data, a density relation value related to density of the target pixel; selecting a conversion color for an object pixel among the plurality of target pixels; converting pixel value of the object pixel to generate converted data such that, the higher the density represented by the density relation value of the object pixel, the closer the pixel value of a pixel of the converted data to a value indicative of the conversion color for the object pixel; and outputting the converted data.

Abstract: A mechanism is described for facilitating intelligent detection of body segmentation for enhanced gesture recognition on computing devices according to one embodiment. A method of embodiments, as described herein, includes receiving an image, dividing the image into components representing regions of the image, determining orientation and a centroid relating to each component, facilitating generation of hypothesis cuts within hysteresis points, and calculating a first ratio based on an average width of the hypothesis cuts and a length of a first axis of a component. The method may further include segmenting the component at one of the hypothesis cuts to determine an intermediate cut of the component, if the first ratio is greater than a predetermined threshold. The method may further include iteratively segmenting the component to determine a final cut.

Abstract: A device determines multiple sets of parameter values associated with an array of pixels to be used to process an image. Each set of parameter values corresponds to a set of pixels, included in the array, that are aligned along a first dimension of the array. The device determines a set of median values corresponding to each of the multiple sets of parameter values. The device determines a median of medians value that is a median of the set of median values. The device excludes a portion of parameter values, included in the multiple sets of parameter values, from a comparison set used to determine an overall median value, and determines a first median parameter value of the comparison set. The device determines the overall median value based on comparing the first median parameter value and the median of medians value, and provides the overall median value.

Abstract: A system for enhancing an input image including receiving an input image and filtering the input image with a plurality of non-linear smoothing filters providing a respective plurality of filtered outputs. The system processes a plurality of the filtered outputs with respect to at least one of another of the filtered outputs and the input image to determine a plurality of detail layers. The system filters the plurality of detail layers with a plurality of non-linear smoothing filters providing a respective plurality of smoothed layers. The system adjusts the plurality of smoothed layers in such a manner that regions closer to an edge are enhanced to a lesser extent than regions farther from an edge and combining the adjusted the smoothed layers to provide an enhanced output image.

Abstract: Described are methods, systems, and apparatus, including computer program products for locating one or more markers associated with IT equipment. An image of a scene including the one or more markers is acquired by a mobile computing device. A band-pass filter is applied by the mobile computing device to first pixel data associated with a first pixel in the image to generate a first band-pass filter result, wherein a pass-band of the band-pass filter is based on the light emitted by the one or more markers. A first pixel score is determined by the mobile computing device based on at least the first band-pass filter result. First indicia of the first pixel score is stored by the mobile computing device in a map at a first map location corresponding to a first image location of the first pixel in the image.

Abstract: A method includes implementing, through a processor communicatively coupled to a memory and/or a hardware block, a Bilateral Filter (BF) including a spatial filter component and a range filter component, and implementing the spatial filter component with a low-complexity function to allow for focus on the range filter component. The method also includes determining, through the processor, filter tap value(s) related to the range filter component as a function of radiometric distance between a pixel of a video frame and/or an image and other pixels thereof based on a pre-computed corpus of data related to execution of an application in accordance with a filtering requirement of the pixel by the application. Further, the method includes constraining, through the processor, the filter tap value(s) to a form i×base based on the BF implementation. i is an integer and base is a floating point base.

Abstract: A computer-implemented method for automatically retrieving information regarding optical properties of a scattering medium including receiving a first digital image and a first image quality value associated with the first digital image and sharpness of an edge of the first digital image, producing an optimized image, transforming the optimized image into an optimized optical transfer function, receiving a second digital image and a second image quality value associated with the second digital image and sharpness of an edge of the second digital image, identifying either the first or second digital image as an optimized digital image, and transforming the optimized optical transfer function into an optimized value of the optical parameter.

Abstract: A method for recognition of a predetermined pattern in an image data set recorded by a device for recording of at least two electromagnetic frequency spectra is provided. A first difference value is formed for the image points of the selected area as a function of a difference between a data vector of a corresponding image point and a first reference data vector. A second difference value is formed for an image point of a selected area as a function of a difference between the data vector of this image point and a second reference data vector. A predetermined pattern is recognized when it is determined at least one pattern correlation quantity is below a predetermined threshold value and a local minimum is present.

Abstract: A pattern matching processing unit 10 binarizes input image data 1, compares a binarized pattern with a pattern provided for each of groups, and outputs either first information indicating a group to which a pattern that matches the binarized pattern belongs or second information indicating no match. A 0-degree direction dedicated filter 21, a 45-degree direction dedicated filter 22, a 90-degree direction dedicated filter 23, and a 135-degree direction dedicated filter 24 which are disposed correspondingly to the groups carry out smoothing processes according to the direction of an edge of the image, respectively. When the pattern matching processing unit outputs the first information, a selector 30 selects the output of either one of the filters corresponding to the group as output image data 2, whereas when the pattern matching processing unit outputs the second information, the selector outputs the input image data 1 as the output image data 2.

Abstract: A median filtering apparatus and method for removing noise and improving an image quality with respect to all types of input images are provided. The median filtering apparatus may receive an input of N pieces of data, may form a data set including the N pieces of data, may calculate a difference array having an N×N size based on the N pieces of data in the data set, may sum component values for each column of the difference array, and may calculate an index of a column having a smallest value among sum values that are obtained by the summing operation and that are greater than or equal to a preset value.

Abstract: A method for removing defective pixels using a signal processing scheme with a Bayer pattern, by determining an output value of the inter-channel according to whether or not a difference between a center pixel and an average of neighboring pixels, which have the same color as that of the center pixel, is greater than or equal to a threshold value; and using values of neighboring pixels nearest to a center pixel as an input, obtaining an output value of the cross-channel by finding a median value among a median value of vertical and horizontal lines including the center pixel, a median value of diagonal lines including the center pixel, and a value of the center pixel. Then, a defective pixel is removed, through the use of the output value of the inter-channel, the output value of the cross-channel, and the value of the center pixel.

Abstract: A median filtering method makes a plurality of pixel data into a block of fixed unit area which includes a central pixel and a plurality of ambient pixels adjacent to the central pixel. Pixel data within the unit area is divided into sub unit areas which are smaller than the unit area. The data value of the divided pixel data is arranged in a fixed order for each of the sub unit areas. An intermediate value is determined from the arranged pixel data in each of the sub unit areas. An average value of the intermediate values which are extracted from each of the sub unit areas, and the central pixel data is determined corresponding to the central pixel of the unit area with the average value.

Abstract: An image processing device for performing edge-preserving smoothing includes a reducing unit for reducing an input image; a reduced image smoothing unit for performing a smoothing processing on a reduced image generated by the reducing unit with edges preserved; and an edge-preserving enlarging unit for enlarging an image generated by the reduced image smoothing unit with the edges preserved. The edge-preserving enlarging unit performs a filtering processing by determining a pixel value of a target pixel for the filtering processing based on weighted addition of pixel values of respective reference pixels. Weighting coefficients for the pixel values of the respective reference pixels is set based on differences between a pixel value of the input image corresponding to the target pixel and pixel values of the respective reference pixels and based on distances between the target pixel and the respective reference pixels.

Abstract: Methods and systems for segmenting pixels for wafer inspection are provided. One method includes determining a statistic for individual pixels based on a characteristic of the individual pixels in an image acquired for a wafer by an inspection system. The method also includes assigning the individual pixels to first segments based on the statistic. In addition, the method includes detecting one or more edges between the first segments in an image of the first segments and generating an edge map by projecting the one or more edges across an area corresponding to the image for the wafer. The method further includes assigning the individual pixels to second segments by applying the first segments and the edge map to the image for the wafer thereby segmenting the image. Defect detection is performed based on the second segments to which the individual pixels are assigned.

Abstract: A method of spectral-spatial-temporal image detection is disclosed. In one embodiment, a spectrally differenced image is obtained by computing a difference of at least two intensity values in at least two spectral bands of an image. Further, a spatially filtered spectral image is obtained by applying a spatial median filter to the obtained spectrally differenced image. Furthermore, a temporal image is obtained by determining a temporal pixel value difference using a computed predictive frame difference. In addition, a spectral-spatial-temporal filtered image for detection is obtained by using the obtained spatially filtered spectral image and the temporal image.

Abstract: A system and method for generating a fused image is provided. The system comprises processing circuitry configured to receive a plurality of images, filter each received image using an edge preserving filter, compute a weight for each received image based on the corresponding filtered image and the received image and generate a fused image based on the weights of each received image. The system further comprises a memory device configured to store the fused image.

Abstract: In at least one embodiment of the invention, an apparatus for adaptive edge enhancement of a video signal includes a transient improvement module. The transient improvement module is configured to generate a first adjusted pixel value based on a window of pixel values for pixels surrounding a pixel-of-interest initially having an input pixel value. The apparatus includes an adaptive peaking module configured to generate a second adjusted pixel value based on the first adjusted pixel value and the input pixel value. In at least one embodiment of the apparatus, the adaptive peaking module comprises a high-pass filter configured to generate a pixel adjustment based on the first adjusted pixel value. In at least one embodiment of the apparatus, the adaptive peaking module further comprises a gain path configured to apply at least one adaptive gain value to the pixel adjustment to generate an adaptive adjustment value.

Abstract: A method for reducing specular reflection in an image. The method includes capturing a first exposure of a scan surface using a first of a plurality of light sources, capturing a second exposure of a scan surface using a second of a plurality of light sources, determining which pixels captured by the light sources are speckles, and replacing a value of at least one speckle in the image with a different pixel value.

Abstract: An apparatus of reducing noise includes: an edge determining module determining whether each pixel of an image corresponds to an edge; a low pass filter module performing low pass filtering of the image in two directions and performing low pass filtering only for the pixel determined not to be the edge; a filter selecting module selecting a filter having a flexible size to be applied to a mask region for each of the images low pass filtered based on the average brightness of the entire region and the average brightness of the mask region having a predetermined size; a sigma filter module sigma filtering the mask region using the selected filter for each of the images low pass filtered; and an averaging module averaging the image sigma filtered in the two directions.

Abstract: Systems, methods, and computer readable media for automatically modifying digital image data in accordance with preferences of a user. When a user accesses a set of digital image data over a network using a computer system, the computer system automatically reads an identifier associated with the set of digital image data and applies at least one digital image filter to the set of digital image data, where the at least one digital image filter is correlated with or mapped to the associated identifier. The user is able to define the identifiers, the filters, and the mapping (correlation) between the identifiers and the filters, thus establishing image display preferences for the user.

Abstract: An image file representing at least a portion of a printed document is processed to highlight the differences between foreground material (e.g., text or other characters) from background. The method includes selecting a neighborhood of pixels, determining a weighted average of an attribute values (e.g., luminance) for each pixel, and modifying each pixel's value based on the weighted average. Graylevel scaling, error diffusion, and a bit level conversion are also performed each pixel ends up with either a first attribute value level (e.g., luminance of 0) or a second attribute value level (e.g., luminance of 255).

Abstract: A set of pixels of a background element is identified according to a mask that defines a shape of a foreground element. A color value for a pixel of the foreground element is determined. The determining includes ascertaining a value of a measure of brightness of one or more pixels of a set of pixels of the background element and calculating the color value for the pixel of the foreground element based on the value of the measure of brightness and a value of an adjustable contrast variable. The calculating the color value for the pixel of the foreground element preserves in the foreground element a color component of the one or more pixels of the set of pixels of the background element and increases contrast with the value of the measure of brightness according to the value of the adjustable contrast variable.

Abstract: A method for sharpening a captured image includes the steps of (i) identifying a plurality of edge pixels in the captured image, (ii) reviewing the plurality of edge pixels to identify one or more line pixels, and one or more non-line pixels in the captured image (354), and (iii) sharpening the captured image utilizing a first level of overshoot control for the non-line pixels (362), and utilizing a second level of overshoot control for the line pixels (360) The method also includes the steps of (i) identifying an intensity value for each of a plurality of neighboring pixels that are positioned near a selected pixel in a predetermined pixel window that have the highest intensity values and the lowest intensity values.

Abstract: A signal processing device (201) includes noise reduction units (101), cascade-connected to each other, each of which includes: a signal selection section (31) for selecting a representative value from sampled signals obtained from an input signal by sampling a target signal and signals which are away from the target signal by given intervals; a voltage determination section (51) for determining which of a determined representative value and a voltage of the target signal is larger; and a signal output section (61) for increasing or decreasing the voltage of the target signal depending on a result of the determining and outputs the target signal as the output signal. A combination of intervals between the target signal and the signals excluding the target signal vary from noise reduction unit (101) to noise reduction unit (101). A noise reduction unit on a more upstream side has a larger maximum value of the intervals.

Abstract: An image pickup apparatus that performs accurate correction processing for a pixel that outputs a non-reproducible high-level signal having a continuity with respect to an adjacent pixel is provided. The image pickup apparatus extracts data corresponding to a pixel of an image pickup element that generates an output value equal to or more than a predetermined value, and determines, within a predetermined area of a captured image, whether a predetermined number or more of pixels that generate an output value having the predetermined level are continuously adjacent to one another in the same direction, irrespective of the color received by the pixel. Then, if it is determined that the predetermined number or more of pixels are continuously adjacent to one another in the same direction, the pixels are indicated as targets and the outputs of the target pixels are corrected.

Abstract: Input image data may be subjected to a spatial convolution to produce a plurality of different filter matrices or kernels. Thus, a single system may be capable of producing a plurality of different filter sizes which may be selected based on particular circumstances, uses and described precision. Thus, a single system may provide variable spatial filtering. By reducing the input data matrix size and by reusing components, in some cases, the computational complexity of producing a plurality of symmetrical filters is not significantly greater than that involved in producing only a single filter.

Abstract: An image filter and method of smoothing pixel values. A pixel value of a pixel to be smoothed is compared with block average pixel values of each of a plurality of pixel blocks. The pixel to be smoothed may be downstream from each of the pixel blocks. If the difference between the pixel value and each of the block average pixel values is less than a corresponding sigma threshold value for each of the pixel blocks, a first sigma filter utilizing the block average pixel values is applied to the pixel to be smoothed. If the difference between the pixel value and any one of the block average pixel values is not less than a corresponding sigma threshold value, a second sigma filter is applied to the pixel to be smoothed.

Abstract: A method for calculating an image quality metric for evaluating the quality of a digital image including the steps of denoising the data of the image, identifying edges in the denoised data, determining an edge profile of the edges, determining a grayscale angle for each identified edge in the edge profile that is associated with the edge, and calculating the image quality metric based on a weighted average of the grayscale angles for all the edges.

Abstract: One embodiment of the present invention provides a system for applying a bilateral filter to an image. During operation, the system selects a first region within the image which is associated with a first pixel. Next, the system constructs a first histogram using pixel values within the first region. The system then computes a new value for the first pixel using the current value of the first pixel and the first histogram. The system then selects a second region within the image which is associated with a second pixel. Next, the system determines a non-overlapping region between the first region and the second region. The system then constructs a second histogram using the first histogram and pixel values in the non-overlapping region. Next, the system computes a new value for the second pixel using the current value of the second pixel and the second histogram.

Abstract: The multi-image sharpening and denoising technique described herein creates a clean (low-noise, high contrast), detailed image of a scene from a temporal series of images of the scene. The technique employs a process of image alignment to remove global and local camera motion plus a novel weighted image averaging procedure that avoids sacrificing sharpness to create a resultant high-detail, low-noise image from the temporal series. In addition, the multi-image sharpening and denoising technique can employ a dehazing procedure that uses a spatially varying airlight model to dehaze an input image.

Abstract: The invention discloses an image processing system and method thereof. The image processing system includes an local variance estimator, a noise detector, a spatial de-noise filter. The local variance estimator estimates each pixel of an input image signal to separately output a local variance value of each pixel, and generates a noise threshold according to the local variance values. The noise detector determines which pixel indicates noise or image according to the noise threshold. The spatial de-noise filter filers the pixel indicating noise to generate an output image signal.

Abstract: A median filtering apparatus and related method for finding out a median from 2r+1 input data, which includes 2(r+1) comparators, a counter, a circuit to obtain a minimum, a register, a state machine, and an adder. 2r+1 comparators of the comparators compare input data with a first predetermined value and compare the 2r+1 input data with an output value of the register. The counter counts the number of input data that are not larger than the first predetermined value or the output value of the register of the input data. The circuit finds out a minimum value of the input data that are larger than the first predetermined value or the output value of the register of the input data and a second predetermined value. The remaining one comparator of the 2(r+1) comparators compares the number counted by the counter with “r”.

Abstract: A system for processing video data to improve image quality at low data rates comprising an encoder generating an entropy signal representing the relative entropy of the video data and a plurality of filters each receiving the entropy signal and a threshold setting and filtering the video data if the entropy signal is greater than the threshold setting.

Abstract: A system is provided for processing video images for display. In one example, the system comprises a stream parser for retrieving a compressed main video data and a sub-picture identifier. A video decoder generates a main video image having a main video intensity value for each pixel in the main video. A sub-picture decoder determines a sub-picture image based on the sub-picture identifier. The sub-picture image includes a pixel data for each pixel in the sub-picture image indicative of a sub-picture pixel type including a background pixel type and a pattern pixel type. Each pixel includes a command code indicative of a background contrast level, a pattern contrast level, a background color level and a pattern color level. An intensity formatter generates a sub-picture pixel intensity for each pixel in the sub-picture image.

Abstract: An image processing apparatus suitable for processing a digital image in YCrCb color space, the image having an initial luminance plane Y and two initial Cr, Cb chrominance planes, the processing apparatus including a first block that receives the initial luminance plane Y of the digital image and processes and modifies the initial luminance plane Y in order to provide a modified luminance plane Y in output; a color artifact correction block, operating in parallel with the first block, the correction block receiving the initial planes Y, Cr, Cb of the image and modifying the initial chrominance planes Cr and Cb through a pixel by pixel processing approach with a mobile working window, the correction block having a false colors correction sub-block and a purple fringing correction sub-block, or both, the sub-blocks structured to modify values of the initial Cr, Cb chrominance planes based on information contained in the initial Cr, Cb chrominance planes and also based on information contained in the initial lu

Abstract: An image processing method, for correcting a blur a due to an optical system of an image capturing apparatus, the image processing method including, storing a plurality of representative filters in a memory, selecting a subset of representative filters from the plurality of representative filters based on a pixel position of a pixel of interest in an image, applying each of the selected representative filter to a pixel value of the pixel of interest, and correcting the pixel value of the pixel of interest based on (a) a result of the application of filters, and (b) the pixel position of the pixel of interest.

Abstract: The invention relates to matrix image sensors, and it relates more particularly to a method for correcting the fixed spatial noise of the matrix. The signal (Sij,n) obtained from each pixel (Pij) during a large number of successive images is collected; for each pixel, an approximate value (Mij) of an average of the signal obtained from the pixel during this large number of images is determined; the signal obtained from each pixel is corrected by adding to it the difference between a reference average value (M0) and the approximate average value. The approximate average is obtained by determining high and low limit values taken by the signal obtained from the pixel during the large number of images, and by calculating a median between these high and low limit values.

Abstract: A method includes receiving image data, generating a low-pass image and a high-pass image from the image data, applying dynamic range compression to the low-pass image and not the high-pass image, and adding the high-pass image to the low-pass image after dynamic range compression to create an output image.

Abstract: Systems and methods for block noise detection and filtering are disclosed. One embodiment includes, computing difference magnitudes in pixel values for adjacent pixels in the image. The difference magnitudes can include horizontal difference magnitudes for horizontally adjacent pixels and vertical difference magnitudes for vertically adjacent pixels. One embodiment further includes using normalized sums of the difference magnitudes to determine a set of noise characteristics of the block noise and a set of image characteristics of the image and configuring inputs to the block noise filter using the set of noise and image characteristics.

Abstract: A method for simultaneously optimizing a digital image taken in or through a scattering medium and obtaining information regarding optical properties of the scattering medium is provided. Digital image data are received by a computer. The digital image is evaluated according to an objective image quality metric and a resulting image quality value is compared to a previously stored image quality value for the image. A revised optical transfer function is derived by modeling the optical properties of the medium to be used to generate a restored digital image, which is derived from the original image and the revised optical transfer function. The restored digital image is evaluated according to the objective image quality metric and an optimized restored image is identified. The optical properties associated with the optical transfer function producing the optimized restored image represent a close approximation of the true optical properties of the medium.

Abstract: A method for calculating an image quality metric for evaluating the quality of a digital image including the steps of denoising the data of the image, identifying edges in the denoised data, determining an edge profile of the edges, determining a grayscale angle for each identified edge in the edge profile that is associated with the edge, and calculating the image quality metric based on a weighted average of the grayscale angles for all the edges.