Abstract: An image processor includes a correction signal generator configured to generate a correction signal by calculating a difference between an image and an image obtained by applying an unsharp mask generated based on a PSF corresponding to an image-pickup conditions of an image-pickup optical system to the image, and a correction signal applier configured to sharpen the image by multiplying the correction signal generated by the generator by a constant and by adding a multiplied correction signal to the image.

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: The present disclosure relates to image filtering techniques for enhancing image quality. In one embodiment, two filtering techniques are applied on an image. Firstly, an adaptive weighted median filtering operation is performed on an acquired low contrast image corrupted by impulsive noise. Subsequently, a guided image filtering on the image obtained from adaptive weighted median filtering operation to de-blur and enhance the contrast that ultimately assures to preserve the edges of the images. In addition, the image filtering for enhancing image quality is enhanced by several variations of data adaptive guided image filtering and adaptive window sizes for guided image filtering techniques.

Abstract: A novel modification to the order statistics filters called the Adaptive Weighted-Local-Difference Order Statistics is shown that will act as a generic framework for the design of adaptive filters suitable for specific signal processing applications. To demonstrate the design of filters using this framework two implementations were defined and evaluated: Edge Orientation Adaptive Weighted-Local-Difference Median Filter (EOAWLDMF) and Luminance-Similarity Adaptive Weighted-Local-Difference Median Filter (LSAWLDMF) for restoration of noisy images.

Abstract: In the image processing apparatus, image data is divided into large blocks of a prescribed size and the large blocks are subdivided into small blocks by the dividing unit. The number of isolated points in each large block is then calculated by the large block isolated point calculation unit, and the number of isolated points in each small block is then calculated by the small block isolated point calculation units. It is then determined by the halftone-dot region determination unit whether or not the large block is a halftone-dot region. This determination considers both the number of isolated points in the large block and the number of isolated points in each small block.

Abstract: A method for applying filters to digital images with minimal amplification of image noise, comprising filtering the digital image with an EPDR edge-preserving detail-reducing filter, determining a matrix from the filtered image as a result of one or more structure adaptive functions, and modifying the digital image using the filter, adjusted by the matrix values, to produce an enhanced digital image. The order of processing may be inverted, by first determining the matrix and then filtering the matrix with the edge-preserving detail-reducing filter.

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 method of coding a moving picture is provided that reduces blocking artifacts. The method can include defining a plurality of defining pixels S0, S1, and S2, which are centered around a block boundary. If a default mode is selected then frequency information of the surroundings of the block boundary is obtained. A magnitude of a discontinuous component in a frequency domain belonging to the block boundary is adjusted based on a magnitude of a corresponding discontinuous component selected from a pixel contained entirely within a block adjacent the block boundary. The frequency domain adjustment is then applied to a spatial domain. Or, a DC offset mode can be selected to reduce blocking artifacts in smooth regions where there is little motion.

Abstract: A method for improving quality of low light video images including: receiving a current video frame; temporally enhancing it by applying a first weight matrix including higher weight factors for stationary regions and lower weight factors for moving regions to the received frame and a reference frame to generate an enhanced temporal video frame; spatially enhancing the enhanced temporal video frame by applying a second weight matrix including higher weight factors for stationary regions and lower weight factors for moving regions to generate an enhanced spatial video frame; and motion enhancing the enhanced temporal video frame by extracting matched rigid moving objects in a previous or future frame and processing each of the extracted matched rigid moving objects with a corresponding rigid object in the enhanced temporal or spatial or raw current video frame.

Abstract: A method and an apparatus for image filtering are described. Structural information is employed during the calculation of filtering coefficients. The structural information is described by the regions defined through an edge map of the image. In one embodiment, the region correlation between the target pixel and a contributing pixel is selected as a structural filtering coefficient. The region correlation, which indicates the possibility of two pixels being in the same regions, is calculated by evaluating the strongest edge cut by a path between the target pixel and a contributing pixel. The structural filtering coefficient is further combined with spatial information and intensity information to form a spatial-intensity-region (SIR) filter. The structural information based filter is applicable to applications such as denoising, tone mapping, and exposure fusion.

Abstract: A method executed by a computer system for detecting edges comprises receiving an image comprising a plurality of pixels, determining a phase congruency value for a pixel, where the phase congruency value comprises a plurality of phase congruency components, and determining if the phase congruency value satisfies a phase congruency criteria. If the phase congruency value satisfies the phase congruency criteria, the computer system categorizes the pixel as an edge pixel. If the phase congruency value does not satisfy the phase congruency criteria, the computer system compares a first phase congruency component of the plurality of phase congruency components to a phase congruency component criteria. If the first phase congruency component satisfies the phase congruency component criteria, the computer system categorizes the pixel as an edge pixel, and if the first phase congruency component does not satisfy the phase congruency component criteria, categorizes the pixel as a non-edge pixel.

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 contour correction device is provided having a video image judging unit which executes an edge detection process to detect a change of a signal in a video image as an edge portion, and a texture detection process to detect a texture portion in which a change of signal smaller than the edge portion in the video image repeatedly appears, and a contour component gain adjusting unit which applies contour correction processes which differ from each other to the edge portion and the texture portion.

Abstract: A method of multi-frame image noise reduction suitable for an image-capturing device includes following steps: obtaining a current frame and multiple reference frames; defining a mask and a target point in the mask; judging whether the target point pixel of the current frame is on an edge according to an edge map of the frame; when the pixel is on the edge, using the pixels in the reference frames on the edge to calculate a replacement result; when the target point pixel is not on the edge, using the pixels in the reference frames surrounding the target point to calculate a replacement result; after that, generating a pixel corresponding to the position of the target point in an output image according to the replacement result; further, moving the mask and going back to judge whether the pixel of the target point of the current frame is on the edge.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: Embodiments and processes of computer tomography perform tasks associated with denoising a reconstructed image using an anistropic diffusion filter and adaptively weighting an iterative instance of the diffused image based upon the product of a weight value and a difference between the iterative instance of the diffused image and the original image. In general, the adaptive weighting is a negative feedback in the iterative steps.

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: Embodiments provide a filtering method and device in image processing. The device includes: a dividing unit to divide a first filtering window into blocks; a determining unit to determine a filtering window area according to relevance between a central block and other non-central blocks in the blocks; and a filtering unit to perform filtering processing on an image in the determined filtering window area. With the method and device of the embodiments, a boundary of an object will not be crossed in filtering, and in removing noise, information on other objects will not be introduced to affect the result of filtering. A filtering window size may be determined while filtering is performing, which may be carried out in one path, avoiding time delay resulting from execution of two paths of a conventional method where an edge is detected and then the size of a filtering window is determined.

Abstract: One or more embodiments of the invention relate to an image processing system and method for filtering with an adaptive filter core size, the method including: an original image is created, an information measure is calculated on the basis of the original image, a filter core size is calculated on the basis of the information measure, the original image is low-pass filtered with an adaptive low-pass filter with the filter core size to form a low-pass filtered image, a high-pass filtered image is calculated by subtracting the low-pass filtered image from the original image, a detail-enhanced image without light rings is obtained by a high-pass image scaled with a detail enhancement measure being added to the low-pass image. Embodiments additionally relate to an image processing device having an image recording device, an image processing unit, and an image display unit.

Abstract: A method for correcting a defect pixel includes extracting a pixel value of a central pixel, and pixel values of each of a plurality of neighboring pixels around the central pixel in an image sensor by using a color filter; calculating reference levels by multiplying each the pixel value of the plurality of neighboring pixels by a weight value; calculating a total number of cases where the pixel value of the central pixel is larger or smaller than the reference level as a first comparison value or second comparison value, respectively; determining the central pixel is a defect pixel where the first or second comparison value is larger than a first or second control register value, by comparing the comparison values with the control register values, respectively; and correcting the central pixel determined as the defect pixel.

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: Embodiments include systems and methods of measuring a blockiness level in video data that has been encoded and decoded. In one embodiment, a Q-value is calculated that indicates the blockiness level of a decompressed image. The Q-value can be based on a block map having indicators corresponding to each of a plurality of pixels in an input image, each indicator signaling if a block edge is present at the corresponding pixel location in the input image, and also based on block locations indicative of the alignment block edge locations in the input image. The Q-value can be calculated in a vertical direction and a horizontal direction with respect to the input image. The Q-value is determined based on a flatness measurement of pixel values around certain considered pixels in the input image. The Q-value can be provided to a deblocking filter to improve the performance of deblocking the input image.

Abstract: An apparatus and method for filtering depth information received from a capture device. Depth information is filtered by using confidence information provided with the depth information based an adaptively created, optimal spatial filter on a per pixel basis. Input data including depth information is received on a scene. The depth information comprises a plurality of pixels, each pixel including a depth value and a confidence value. A confidence weight normalized filter for each pixel in the depth information is generated. The weight normalized filter is combined with the input data to provide filtered data to an application.

Abstract: A method and apparatus are provided for adaptive sharpness enhancement of image data. In on embodiment, a method for sharpness enhancement includes receiving image data for a first frame, performing linear sharpening enhancement of the image data, performing non-linear sharpening enhancement of the image data, and generating blending parameters based on the image data. The blending parameters may be generated based upon image data of the first frame, linear sharpened image data for the first frame, and non-linear sharpened image data for the first frame. The method may further include blending the image data of the first frame, the linear sharpened image data, and the non-linear sharpened image data based upon the blending parameters.

Abstract: A method and an apparatus are provided for eliminating noise in a digital image. An ambient image and at least one flash image are captured in succession of a given location. At least one image alignment technique is applied for the ambient image and the at least one flash image. Joint mean shift filtering is applied to the at least one flash image to obtain filter weights. The filter weights are applied to the ambient image to eliminate noise in the ambient image.

Abstract: A method and system are provided in which a processor, such as a video processor, may determine a flatness value for a current video picture and may adjust a mosquito noise filtering of a subsequent video picture based on the determined flatness value. The flatness value may be determined within a predetermined region of the current video picture, which may be dynamically modified by the processor. The flatness value may be associated with the presence of analog noise in the current video picture, and may be determined based on horizontal and vertical variance values determined for one or more portions of the current video picture. The processor may adjust a mosquito noise strength value of the current video picture based on the determined flatness value and may adjust the mosquito noise filtering of the subsequent video picture based on the adjusted mosquito noise strength value of the current video picture.

Abstract: In an imaging device having an objective and a stage for holding a sample to be imaged, a method for autofocusing is presented. The method includes determining a measured focus value corresponding to at least a first of a plurality of logical image segments. Further, the method includes imaging the first logical image segment using the measured focus value. The method also includes determining a predicted focus value for a second of the plurality of logical image segments using the measured focus value and a stored focus variation parameter. In addition, the method includes imaging the second logical image segment using the predicted focus value.

Abstract: A method for eliminating image blur includes: detecting the difference in pixel value between two corresponding pixels in two continuous images to generate a difference value; and adjusting the luminance of the two corresponding pixels according to the difference value, wherein when the difference value exceeds a predetermined value, the luminance of one pixel of the two corresponding pixels is increased and the luminance of the other pixel is decreased.

Abstract: Techniques in accordance with the following disclosure enable digital images to be filtered (smoothed) to reduce noise and, at the same time, preserve the image's underlying structure. In general, image pixels are analyzed to identify those that participate in, or belong to, structure within the image and those that do not. For those pixels determined to be part of the image's structure, the direction of that structure is determined and filtering or smoothing along that direction is provided. Contrast enhancement in a direction perpendicular to the detected edge's direction may also be provided.

Abstract: An apparatus comprising one or more processors configured to receive a digital image comprising a current pixel and a plurality of nearby pixels, determine a pixel intensity of the current pixel, wherein the pixel intensity comprises a noise component, and reduce the noise component by applying a bilateral filter as a combination of a domain filter and a range filter on the current pixel, wherein the domain filter is dependent on the pixel intensity and geometric closeness between the current pixel and the nearby pixels, and wherein the range filter is dependent on the pixel intensity and photometric similarity between the current pixel and the nearby pixels.

Abstract: Provided is an image processing apparatus including: a frequency value calculation section that allocates each pixel of an input image to any of respective partial regions obtained by dividing the entirety of a possible range of a luminance value into units in a luminance direction on the basis of the luminance value thereof, and allocates one pixel of the input image to partial regions when calculating frequency values representing the number of pixels allocated to the partial regions with respect to the respective partial regions, to update the frequency values of the partial regions; a characteristic value calculation section that calculates a characteristic value representing a characteristic of the partial region; and a weighted product-sum section that performs edge-preserving smoothing on the input image by weighting and averaging the characteristic values in accordance with a distance in the luminance direction, using the calculated frequency value and the calculated characteristic value.

Abstract: Accurately estimating the image quality of video is still difficult, especially when there is no reference video available. A method for determining a quality measure for a video image comprises selecting a measuring point, such as a MB, determining a context area around the measuring point, calculating a variance of pixel values in the context area, calculating a variance of pixel values in the measuring point, calculating a relation between the two variances of pixel values, and averaging said relations for a plurality of measuring points, wherein a quality measure for a video image is obtained.

Abstract: An infrared detector system is described in which a despeckle filter is applied to image data generated by a High Operating Temperature (HOT) detector array. The filter reads the data associated with each pixel of the image generated and compares it with selected neighbouring pixels. The comparison yields a series of values that are compared to predetermined thresholds and the pixel is scored according to the number of values that exceed the threshold set. The score assigned to the pixel then determines the treatment of the pixel in the image to be generated. The data value of the pixel may be ignored, included or substituted with an alternative calculated value.

Abstract: A prediction error (eq[x,y]) is added to a predicted frame ({circumflex over (f)}[x,y]) or a predicted block for receiving a decoded frame (gq[x,y]) or a decoded block to be further used in a prediction loop by an encoder or to be sent to the output of a decoder. The reference frame (gq[x,y]) or the reference block includes a useful signal part and a noise signal part. The reference frame (gq[x,y]) or reference block pass through a dedicated noise reducing filter to reduce or eliminate the noise signal part of the reference frame (gq[x,y]) or reference block.

Abstract: A method for applying filters to digital images with minimal amplification of image noise, comprising filtering the digital image with an EPDR edge-preserving detail-reducing filter, determining a matrix from the filtered image as a result of one or more structure adaptive functions, and modifying the digital image using the filter, adjusted by the matrix values, to produce an enhanced digital image. The order of processing may be inverted, by first determining the matrix and then filtering the matrix with the edge-preserving detail-reducing filter.

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: A method of obtaining variance data or standard deviation data for efficiently reducing noise in an image is provided. The method includes (a) dividing an image into a plurality of clusters; (b) checking frequency data of data values of pixels included in each cluster; (c) obtaining a weighted average of the data values of the pixels included in each cluster; (d) obtaining a variance or a standard deviation of the data values of the pixels included in each cluster by using the weighted average; and (e) obtaining variance data or standard deviation data in accordance with weighted averages by using the weighted average and the variance or the standard deviation in each cluster. A digital photographing apparatus is also provided that includes a recording medium storing variance data or standard deviation data for efficiently reducing noise in an image which is obtained in accordance with the above method.

Abstract: One or more systems and methods for edge-adaptive and recursive non-linear filtering of ringing effect on image or video data are disclosed in accordance with various embodiments of the invention. In one embodiment of the invention, a method for edge-adaptive and recursive non-linear filtering of ringing effect first involves detecting all edges within a process area of an image, including a direction of an edge slope and a value of an edge signal level. Then, if the process area is determined to be a “non-busy” area based on a “busyness” measure relative to empirically-defined threshold values, then an edge influence function subsequently determines whether to apply a de-ringing filter to a current pixel within the process area or not. Preferably, the de-ringing filter is edge-adaptive, non-linear, and recursive. The de-ringing filter can adjust the current pixel multiple times based on filter angles, adjacent pixels, and pixel transition levels.

Abstract: Systems, methods, and software for operating an image processing system are provided herein. In a first example, a method of operating an image processing system is provided. The method includes identifying object pixels associated with an object of interest in a scene, identifying additional pixels to associate with the object of interest, and performing an operation based on a depth of the object in the scene on target pixels comprised of the object pixels and the additional pixels to change a quality of the object of interest.

Abstract: An apparatus and a method of encoding/decoding an image are provided. The method includes generating a bitmap table by mapping a quantized image, that is quantized according to a predetermined bit-depth, to a map table, and setting a bitmap index corresponding to each pixel location of the quantized image with reference to the bitmap table; setting a fixed filter index corresponding to an image of each pixel of the image by analyzing local characteristics of the image; generating bitmap data by adding the filter index to the bitmap index; extracting a bitmap index, a bitmap table, and a filter index from the bitmap data; extracting an encoded image mapped to the bitmap index from the bitmap table; and filtering the extracted encoded image based on a filter corresponding to the filter index.

Abstract: A method for performing fast detail-preserving filtering of an input digital image includes, for each pixel in the image, calculating the difference between the selected pixel and each of its four neighboring pixels located above, left, right, and below the selected pixel, calculating a scaled weighted sum of differences between the actual pixel and its four neighbors, where for each neighboring pixel the weight is an edge-sensing function having a data-adaptive scaling parameter function, and adding the weighted sum of differences to the value of the selected pixel.

Abstract: A method is provided that includes generating coefficients of a scene adaptive filter (SAF) based on differences between values of neighboring pixels in a representative two dimensional (2D) image, and applying the SAF to a plurality of corresponding 2D images.

Abstract: A method of transmitting video data includes receiving a frame of video data of a first resolution from an image capture device at a video transmission device and generating interlaced fields of the frame of video data at the video transmission device by applying a filter with a different set of filter coefficients to at least one field than is applied at least one other field. A method of producing video data of a particular resolution includes generating estimated high resolution video data from low resolution video data received at a display device, producing estimated low resolution video data from the estimated high resolution video data by applying a filter to the estimated high resolution video data, differencing the estimated low resolution video data and the received low resolution video data to generate difference data, and using the difference data to generate new estimated high resolution video data.

Abstract: A loop filter section according to an embodiment of the present invention includes: a BF section selectively acting on a block boundary of an image processed block by block; and an adaptive filter section including a first linear filter section that acts on an input image to be supplied to the BF section and a second linear filter section that acts on an output image of the BF section. The adaptive filter section performs addition of an output image of the first linear filter section and an output image of the second linear filter section to output a result of the addition.

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: An input image is denoised by first constructing a pixel-wise noise variance map from the input image. The noise has spatially varying variances. The input image is partitioned into patches using the noise variance map. An intermediate image is determined from the patches. Collaborative filtering is applied to each patch in the intermediate image using the noise variance map to produce filtered patches. Then, the filtered patches are projected to an output image.

Abstract: An optimized adaptive loop filter does not redesign filters inside the optimization loop of signaling depth which saves computations. Additionally, the Sum of Squared Errors (SSE) (distortion) of blocks is computed for the smallest blocks, thus, allowing for the distortion of larger blocks to be computed efficiently by adding block SSEs together which saves computations by removing redundant operations to calculate SSE of a block each time.

Abstract: Video sequence processing is described with various filtering rules applied to extract dominant features for content based video sequence identification. Active regions are determined in video frames of a video sequence. Video frames are selected in response to temporal statistical characteristics of the determined active regions. A two pass analysis is used to detect a set of initial interest points and interest regions in the selected video frames to reduce the effective area of images that are refined by complex filters that provide accurate region characterizations resistant to image distortion for identification of the video frames in the video sequence. Extracted features and descriptors are robust with respect to image scaling, aspect ratio change, rotation, camera viewpoint change, illumination and contrast change, video compression/decompression artifacts and noise.

Abstract: The present disclosure relates to image filtering techniques for enhancing image quality. In one embodiment, two filtering techniques are applied on an image. Firstly, an adaptive weighted median filtering operation is performed on an acquired low contrast image corrupted by impulsive noise. Subsequently, a guided image filtering on the image obtained from adaptive weighted median filtering operation to de-blur and enhance the contrast that ultimately assures to preserve the edges of the images. In addition, the image filtering for enhancing image quality is enhanced by several variations of data adaptive guided image filtering and adaptive window sizes for guided image filtering techniques.