Abstract: A computer implemented method and system determines a center point of a circular object in an image. A circular object identification system identifies prospective center points of the circular object for each of multiple pixel points at a predetermined distance along a gradient direction determined for each of the pixel points, constructs an axis between the corresponding prospective center points and a corresponding pixel point, and locates prospective circumference points at predetermined angles from the constructed axis.

Abstract: A method for predicting whether a test image (318) is sharp or blurred includes the steps of: providing a sharpness classifier (316) that is trained to discriminate between sharp and blurred images; computing a set of sharpness features (322) for the test image (318) by (i) generating a high pass image (404) from the test image (318), (ii) generating a band pass image (406) from the test image (318), (iii) identifying textured regions (408) in the high pass image, (iv) identifying texture regions (410) in the band pass image, and (v) evaluating the identified textured regions in the high pass image and the band pass image to compute the set of test sharpness features (412); and evaluating the sharpness features using the sharpness classifier (324) to estimate if the test image (318) is sharp or blurry (20).

Abstract: A content-adaptive edge and detail enhancement method is described for image/video processing. Both 2D peaking and LTI/CTI are used in sharpening pictures. Image analysis is performed to generate a mask to control the use of the two peaking techniques. The strength or likelihood of edges or transitions is measured and such a strength or likelihood measurement will be transformed into a blending factor controlling the blending of the LTI/CTI and peaking outputs.

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 apparatus is described. The apparatus includes a digital camera that is capable of shooting at least two images at different effective lens focal lengths. The camera is also capable of high pass filtering (HP) said at least two images, estimating respective spectral densities (SD) of said at least two filtered images and then low pass filtering (LP) the respective estimates of said at least two filtered images prior to combining said at least two images into a single image to produce one or more optical effects.

Abstract: Systems, methods, and computer readable media to approximate edge-preserving transformations with global transfer functions are described. In general, a first transfer function that approximates an edge-preserving operation can be found which, together with an enhancement filter (e.g., dynamic range compression) may be used to generate a global transfer function. Alternatively, a second transfer function may be found that approximates the behavior of the combined first transfer function and enhancement filter. Together the first and second transfer functions may generate a global transfer function. It has been determined that a down-sampled version of an input image may be used to develop the global transfer function. Application of global transfer functions in accordance with this disclosure can generate an output image that exhibits the same overall tonality of the input image without introducing the loss of detail and other artifacts attributable to local processing (e.g.

Abstract: The appearance of image details can be preserved and/or enhanced by applying contrast adaptive gain to the high spatial frequency component of the luminance information. The image details in bright and/or dark regions can be further boosted by applying a local mean adaptive gain. The contrast transfer mapping curve for luminance contrast enhancement can be re-scaled to account for the applied gain. The re-scaling may be performed from frame to frame of displayed video. The re-scaling may be temporally controlled for subsequent frames to make the re-scaling change gradually to prevent flickering.

Abstract: A method and an apparatus for single-image-based rain streak removal are introduced herein. In this method and apparatus, an original image is decomposed into a high frequency part and a low frequency part, and the high frequency image is then decomposed into a rain part and a non-rain part. The non-rain high frequency image and the original low frequency image are used to produce a non-rain image.

Abstract: A contour correcting device which includes a plurality of delay elements which delay an input video signal, a pixel-selection-control-signal generation circuit, a pixel selection circuit which selects outputs of the plurality of delay elements in response to outputs of the pixel-selection-control-signal generation circuit, a high-pass filter operation circuit and an adder circuit which adds an operation result of the high-pass filter operation circuit to the input video signal. When the pixel selection circuit extracts a contour component in a boundary portion between a horizontal video effective period and a period other than the horizontal video effective period of the input video signal, the pixel selection circuit replaces pixel data in the period other than the horizontal video effective period among pixel data input to the high-pass filter operation circuit, with pixel data at an edge-point of the horizontal video effective period.

Abstract: A system, method, and computer program product for applying a spatially varying unsharp mask noise reduction filter is disclosed. The spatially varying unsharp mask noise reduction filter generates a low-pass filtered image by applying a low-pass filter to a digital image, generates a high-pass filtered image of the digital image, and generates an unsharp masked image based on the low-pass filtered image and the high-pass filtered image. The filter also blends the low-pass filtered image with the unsharp masked image based on a shaping function.

Abstract: A weighted image enhancement method includes receiving an original image. The original image includes several original pixels. The original image is sharpened to generate a sharpened image. The sharpened image includes several sharpened pixels. Edge detection is performed with respect to the original image to generate a probability of whether each original pixel is on an edge. An enhancement mode setting is received. A corresponding weight table corresponding to the enhancement mode setting is looked up to obtain a corresponding enhancement weight of each original pixel according to the probability of whether each original pixel is on an edge. A weight calculation is performed utilizing each original pixel and its corresponding sharpened pixel according to its corresponding enhancement weight to generate an enhanced image. The enhanced image is displayed by a display unit.

Abstract: An image processing apparatus includes: a high-pass filter that extracts a high-frequency component of an input image; an edge judgment unit that judges whether there is an edge based on the high-frequency component and estimates an edge position when there is an edge; a coordinate conversion unit that obtains an edge position in an enlarged image corresponding to the estimated edge position; an interpolation unit that obtains, regarding a position where an edge position of the high-frequency component and the edge position of the enlarged image overlap, a pixel value at each pixel position of an edge circumference of the enlarged image using a pixel value of the high-frequency component; a first enlargement processing unit that generates the enlarged image by enlarging by a desired enlargement factor; and a synthesis unit that synthesizes the interpolated pixel value of the high-frequency component and the pixel value of the enlarged 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: Various embodiments describe image sharpening techniques that automatically estimate a sharpening amount for an unsharp mask filter for image enhancement based upon a statistical correlation between detail coefficients at a first resolution and at a second resolution. In various embodiments, statistical deviation of absolute values of detail coefficients for both the image at full resolution and the image downsampled by a pre-determined factor, e.g. a factor of two (half resolution), are retrieved. In various embodiments, the statistical deviation is retrieved for a histogram of the image at the first resolution and a histogram of the image at the second resolution. The linear model between the statistical deviation of detail coefficients at the first resolution and the second resolution is used to calculate the sharpening amount and the unsharp mask filter is applied to the image to produce a sharpened image.

Abstract: According to one embodiment, an image processing device includes a line memory that stores an input image by a plurality of rows; a defect correcting circuit that performs defect correction on the input image based on image data stored in the line memory; a binning circuit that generates a low pass image having a lower spatial frequency than the input image by binning the input image subjected to the defect correction; a frame buffer that stores the low pass image; a filter that generates a high pass image having a higher spatial frequency than the low pass image by filtering the input image subjected to the defect correction; and a mixing circuit that mixes the low pass image with the high pass image.

Abstract: An image processing method includes the steps of obtaining an input image that is an image in which information of an object space is obtained from a plurality of points of view using an image pickup apparatus that includes an image pickup element having a plurality of pixels and an imaging optical system, calculating a first position of a virtual imaging plane that corresponds to a specified focus position, setting the virtual imaging plane to a second position that is in a range of a depth of focus of the imaging optical system with reference to the first position so that a maximum value of an apparent pixel pitch that is formed by reconstructing the input image is decreased, and generating an output image in a state where the virtual imaging plane is set to the second position.

Abstract: A method for detecting a block raster in an image comprising a number of pixels, each of which having assigned at least one pixel value, said pixels being arranged one after the other along horizontal and vertical pixel boundaries. The method includes establishing a first raster with raster limits (xj+dxj) that run either parallel to the vertical pixel boundaries or to the horizontal pixel boundaries and, the position of said limits being predetermined by way of an offset (OF) and an opposite distance (SP). A raster scale is determined of the raster as a function of the edge scales of at least some of the raster limits (xj,+dxj). The method is repeated for a second raster, and the first or second raster is selected in consideration of the raster scales.

Abstract: An apparatus for adjusting images is provided. The apparatus comprises an image decomposing unit for decomposing an input image I into high-pass images HP1, HP2, . . . , HPN and a low-pass image LPN, and an image adjusting values determining unit for determining high-pass image adjusting values ?1, ?2, . . . , ?N for adjusting the high-pass images HP1, HP2, . . . , HPN and a low-pass image adjusting value ?N for adjusting the low-pass image LPN. The apparatus further comprises an image adjusting unit for adjusting the high-pass images HP1, HP2, . . . , HPN using the high-pass image adjusting values ?1, ?2 . . . , ?N and the low-pass image LPN using the low-pass image adjusting value ?N, and an image recomposing unit for recomposing the adjusted high-pass images HP1*, HP2*, . . . , HPN* and the adjusted low-pass image LPN* into an adjusted image A.

Abstract: Disclosed are a depth image noise removal apparatus based on a camera pose, which includes: a depth image obtaining unit for obtaining a plurality of depth images; a camera pose converting unit for converting camera poses of the plurality of depth images into a camera pose of a reference depth image; and a depth image filtering unit for filtering the reference depth image by using a weighted average of each pixel of the reference depth image, and a method using this apparatus.

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: An image processor is capable of performing an image dead pixel detection method, so as to detect at least one dead pixel in an input image. The image dead pixel detection method includes the following steps. A high-pass filter processing is performed on an input image, so as to obtain a filter image. A global mean value and a global standard difference are obtained according to the filter image. A to-be-detected pixel in the filter image is selected. A local mean value, a local standard difference, and a global mean value difference and a local mean value difference are obtained according to the to-be-detected pixel and multiple neighboring pixels. When the global mean value difference and the global standard difference satisfy a first condition, and the local mean value difference and the local standard difference satisfy a second condition, the to-be-detected pixel is considered as a dead pixel.

Abstract: Provided is an image processing apparatus that obtains an effect of improving perceived definition even when an input image signal does not include much of a high range component.

Abstract: The present invention relates to a method and a device for enhancing a digital image comprising samples or pixels. The method comprises: defining a patch of samples surrounding any given sample; computing distance values representing similarity between patches of a first sample and other samples to obtain a distance map; computing, using the distance map, weights to obtain a weight map; applying a filter enhancing high spatial frequencies to the weight map to obtain an enhanced weight map; filtering the first sample by averaging the samples using the enhanced weight map, to obtain an enhanced first sample. The invention provides a very efficient enhancement of the digital image since coherent structures that would usually not influence the enhancement of the image, are detected and made more significant thanks to the filtering of the weight map.

Abstract: A method for producing high-quality photographic images in low-light conditions and in the absence of large-aperture optics. The method includes, upon photographing, first obtaining a plurality of frames of the image with exposures which either partially overlap in time or with an insignificant pause between them. The best result can be obtained in the case when the pause between the exposures represents less than 1/20 of the overall exposure time. The method further includes separating out the initial images from a group of exposures and filtering the images having the smallest exposure interval using the images having the largest exposure interval. The final image is obtained by combining initial images having different exposure intervals from the same group.

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 of improving an image quality of a video signal, which is supplied on a frame-by-frame basis, includes extracting low frequency components from a video signal of an n-th frame, where n is a natural number excluding zero, subtracting the extracted low frequency components from a video signal of an (n+m)th frame to produce high frequency components, where no is a natural number excluding zero, and adding the produced high frequency components to the video signal of the (n+m)th frame. The extracted low frequency components include a horizontal component and a vertical component.

Abstract: There are provided an apparatus and a method for estimating edge areas of pixels in a digital image to thereby prevent an edge sharpening algorithm from being applied to non-edge area of the digital image. Therefore, the apparatus can accurately determine whether each pixel is in an edge area or in a non-edge area, by generating a binary mask obtained by using a luminance difference average between each pixel and each of neighboring pixels in the digital image. Moreover, the determination of an edge-area or a non-edge area may be applied to various digital images, by adjusting a level of edge-area determination.

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: An image processing apparatus creates a basic-structure component image representing an image that an input image except edge is smoothed while preserving the edge in the input image; and creates a detail component image representing an image that the basic-structure component image is subtracted from the input image. Subsequently, the image processing apparatus decomposes the detail component image into a plurality of frequency-band images through multiresolution transform processing, and performs noise removal on the obtained frequency-band images. After that, the image processing apparatus performs inverse multiresolution transform processing of combining the noise-removed frequency-band images, thereby creating a noise-removed detail-component image.

Abstract: Various embodiments of methods and apparatus for image deblurring and sharpening using local patch self-similarity are disclosed. In some embodiments, an input blurred image is down-sampled to generate a downsized image. The downsized image is convolved with a blur kernel to obtain a smoothed image. For each of a plurality of patches of the input blurred image, a corresponding patch in the smoothed image is found. High frequency components between each of the plurality of corresponding patches in the smoothed image and corresponding patches of the downsized image are computed. The high frequency components are applied to the plurality of patches of the input blurred images to generate a deblurred version of the input blurred image.

Abstract: An image processing apparatus includes a calculation to calculate a compression gain that is applied to a low-frequency component of an input image and an amplification gain that is applied to a high-frequency component of the input image, a generation to generate a display image in which a pixel value of a pixel of the input image is corrected based on the compression gain and the amplification gain calculated by the calculation unit, and a display to display the display image.

Abstract: Systems, methods, and apparatus for sampling images using edge detection are presented herein. A gradient component can calculate at least one gradient of a luminance of a block of pixels based on at least one direction; and select a minimum gradient of the at least one gradient of the luminance. Further, a direction component can determine a direction of the block based on a direction of the minimum gradient of the at least one gradient of the luminance. Moreover, a sampling component can alternately select subpixels of the block based on the direction of the block. In addition, a filter component can calculate at least one gradient of a color of a subpixel of the subpixels based on the at least one direction; determine a direction of the subpixel based on the at least one gradient of the color; and filter the subpixels based on the direction of the subpixel.

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: A signal processing device first decomposes an input signal into a plurality of frequency components in different frequency ranges, then causes a higher harmonic wave generating section to generate a higher harmonic for each of a part or all of frequency components obtained by removing a frequency component in the lowest frequency range from frequency components obtained by the decomposition, and finally composes (i) the higher harmonic thus generated and (ii) a frequency component for which no higher harmonic has been generated. The higher harmonic wave generating section adds the frequency component and a nonlinear process signal (i) in which positive and negative signs of a frequency component for which the higher harmonic is to be generated are retained and (ii) which broadly monotonically increases nonlinearly with respect to the frequency component when the frequency component is located at least in the vicinity of zero.

Abstract: A signal processing device (500) that processes sharpening of an image with respect to an input signal (SR) that represents the image and outputs an output signal (SO) that represents the sharpened image includes an oversampler (200) that generates an oversampled signal (S200) by interpolating a signal in order to increase a sampling frequency with respect to in input signal (SR), and a sharpening processing unit (100) to which the oversampled signal (200) is inputted and which generates a sharpened signal (S100) which is nonlinearly monotonically increased over a wide range high band frequency components in the oversampled signal (S200), and the sharpened signal (S100) is outputted as the output signal (SO).

Abstract: An image processing device includes a signal supplementing unit that generates a harmonic signal of a signal of a predetermined frequency band in an image signal, and supplements the image signal with the generated harmonic signal.

Abstract: A pixel interpolation process is based on detection of a potential edge in proximity to a pixel being estimated, and the angle thereof. The potential edge and its angle is determined based on filtering of offset or overlapping sets of lines from a pixel window centered around the pixel being estimated and then cross-correlating the filter results. The highest value in the correlation result values represents a potential edge in proximity to the pixel being estimated and the index of the highest value represents the angle of the potential edge. This information is used in conjunction with other information from the cross-correlation and analysis of the differences between pixels in proximity to verify the validity of the potential edge. If determined to be valid, a diagonal interpolation based on the edge and its angle is used to estimate the pixel value of the pixel. Otherwise, an alternate interpolation process, such as vertical interpolation, is used to estimate the pixel value for the pixel.

Abstract: Embodiments of the present application automatically utilize parallel image captures in an image processing pipeline. In one embodiment, image processing circuitry concurrently receives first image data to be processed and second image data to be processed, wherein the second image data is processed to aid in enhancement of the first image data.

Abstract: An image enhancement system and method include a high-pass filter (HPF) configured to pass high-frequency spatial components of an input image to generate a high-pass image; an adder configured to add the high-pass image to the input image, thereby resulting in an enhanced image; and an over-enhancement detection unit configured to detect tendency of the input image towards overshoot or undershoot. An output of the over-enhancement detection unit is used to prevent overshoot or undershoot in the enhanced image.

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: A video signal processing apparatus includes: an enhancement gain generating section obtaining an enhancement gain for each pixel based on an luminance signal forming part of an input video signal and a predetermined enhancement gain; and an enhancing section performing a process of enhancing the luminance signal forming part of the input video signal based on the enhancement gain. The enhancement gain generating section includes a mean deviation calculating portion obtaining a mean deviation of luminance values of pixels included in a predetermined region centered at a pixel of interest that is the pixel for which the enhancement gain is to be obtained, a weighting coefficient generating portion generating a weighting coefficient for the pixel of interest according to the value of the mean deviation, and a multiplication portion multiplying the predetermined enhancement gain by the weighting coefficient to obtain the enhancement gain for the pixel of interest.

Abstract: A method and system for generating a cartoon are provided. The method includes receiving a digital image for processing at a server. The method includes executing a color structure coding procedure to produce a color-reduced interim image. The method includes generating a sketch from the digital image with a Difference of Gaussian procedure. The method includes combining the color-reduced interim image and the sketch into a processed cartoon.

Abstract: An auto-focus image system includes a focus signal generator and a pixel array coupled thereto that captures an image that includes a plurality of edges. The generator computes a focus signal from a plurality of edge-sharpness measures, each measured from and contributed by a different edge as a quantity with a unit that is a power of a unit of length. The generator reduces a relative weight of the contribution of an edge depending on a shape of a normalized gradient profile of the edge as identified by an n-tuple of values of n different shape measures (n?2). Each shape measure varies across normalized gradient profiles of different shapes. One shape measure may be the edge-sharpness measure itself. The weight may be zero if the n-tuple falls outside a predetermined region. At least one symmetrical shape that has perfect reflection symmetry receives reduced weight.

Abstract: Embodiments of the present invention provide an image sharpening method and device. The method includes performing bilateral filtering processing and difference of Gaussians filtering processing on original image information to obtain first image-layer information and second image-layer information respectively. The first image-layer information is subtracted from the original image information to obtain third image-layer information. Fusion and superimposition processing is performed on the second image-layer information and the third image-layer information to obtain fourth image-layer information. The original image information and the fourth image information are added to obtain processed image information.

Abstract: The present invention discloses a method for decomposing a target pattern containing features to be printed on a wafer, into multiple patterns, the features having a plurality of patterns within a minimum pitch for processes utilized to image the target pattern. The method includes superposing a predefined kernel over a pixel, and moving the kernel from one pixel to another, the pixels representing the sub-patterns of the target pattern. Polarity of the kernel may be reversed when the pixel has a stored intensity value that is negative.

Abstract: There is provided an apparatus for improving the sharpness of an image, which may prevent occurrence of distortion of an image in an edge enhancement process of the image by applying an active weight in accordance with a two-dimensional (2D) high pass filtering value of the image. The apparatus may include: a 2D high pass filter outputting a high frequency element value for the luminance values of pixels of an input image; a weight generating unit generating a weight changed depending on a magnitude of the high frequency element value; a weight applying unit applying the weight to the high frequency element value; and an edge enhancement image generating unit adding, to the luminance values of the pixels of the input image, the high frequency element value to which the weight is applied to thereby output an image of which an edge is enhanced.

Abstract: An information processing apparatus has a decomposition unit that decomposes an image into multiple frequency component images, a reduction unit that reduces linear noise included in the frequent component images, and a reconstruction unit that reconstructs the frequency component images with reduced linear noise.

Abstract: Method for detecting edge of fixed pattern includes receiving and analyzing a first image to obtain a first edge information. Second image and a corresponding second edge information are received, in which the second image includes an accumulation of image history information. According to the first edge information and the second edge information, a consistent number and an inconsistent number for pairs of pixels at the corresponding location of the first image and the second image are calculated, in which the consistent number represents how many pairs of pixels of which two compared pixels of each pair are both edge pixels, and the inconsistent number represents one of the two compared pixels is not the edge pixel. When the consistent number is greater than first predetermined value and meanwhile the inconsistent number is less than second predetermined value, first image and second image have a fixed pattern with fixed edge.

Abstract: Methods, machines, and computer-readable media for processing an input image with a bi-selective filter in a transform domain are described. In one aspect, a forward transform of an input image is computed. A bi-selective filter smoothes image regions with low magnitude coefficients and sharpens image regions with high magnitude coefficients. An output image is generated from the filtered forward transform.

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.