Abstract: A method for enhancing an input image to produce an enhanced output image is provided. The method includes constructing a photographic-mask intermediate image without low-contrast details and a temporary-image intermediate image with enhanced mid-contrast details, retained high-contrast details, and reduced low-contrast details, and employing values for the photographic-mask intermediate image and temporary-image intermediate image to produce the enhanced output image that is globally and locally contrast-enhanced, sharpened, and denoised.

Abstract: Provided are, among other things, systems, methods and techniques for processing an image. In one representative implementation, input image values are obtained for an input image; values for image parameters are measured across the input image values; target values for the image parameters are input; a transformation is applied to the input image values to produce corresponding output image values, the transformation having been generated as a result of a plurality of individual image-value operations that have been constrained by the target values in order to control the image parameters across the output image values; and a processed output image is output based on the output image values.

Abstract: A computer implemented method of mapping values of source pixels (c(x)s) of a source image 2 onto target pixels (c(x)t) of a target image 4. In one image (2) or in respective images (2, 4) two different groups (Rs, Rt) of pixels are selected to be representative of target and source pixels according to their property values. Within the image or images, target pixels (x?T) and source pixels (x?S) are selected which match the selected representative target and source pixels according to the property values thereof. The distributions of values of properties associated with the source pixels and target pixels are calculated. New property values are mapped onto the target pixels according to a transform which minimises an overall closeness measure between the source distribution and the target distribution.

Abstract: Embodiments of the present invention are directed to efficient video processing methods and systems for computationally efficient denoising, sharpening, contrast enhancement, deblurring, and other spatial and temporal processing of a stream of video frames. Embodiments of the present invention separate statistics-related calculations, including estimation of pixel-value-associated variances, standard deviations, noise thresholds, and signal-contrast thresholds, carried out on only a small percentage of video frames selected at a fixed or variable interval from the video stream, from various spatial and temporal processing steps carried out on each frame of the video stream. In certain embodiments of the present invention, the statistics-related calculations are carried out by the general processor or processors of a computer system, while the frame-by-frame spatial and temporal processing is carried out by one or more specialized graphics processors within the computer system.

Abstract: Provided are systems, methods and techniques which use local image properties to determine which potential defects in an image should be corrected. In one representative embodiment, potential defects in an image are identified based on edge sharpness, and measures of strength for the different potential defects are calculated. The measures of strength for the potential defects are then evaluated in view of certain local properties of the image, in an immediate vicinity of such potential defects, in order to identify the image defects to be corrected.

Abstract: Representations of an input signal at different lower resolutions are obtained, where n denotes a level of resolution, and where 0?n?N. Level n=N corresponds to the representation having the lowest resolution, and level n=0 corresponds to the input signal. The resolution increases as level n decreases. The nth level representation is used to estimate and remove low frequency error in the n?1th level representation. Then application-specific processing is performed on the n?1th level representation.

Abstract: Processing of a digital image includes applying a function to a plurality of pixels in the image, the function relating a noise statistic to pixel intensity values. By applying the noise statistic function, a noise statistic value is produced for each of the pixels. Each of the pixels is processed with respect to its noise statistic value.

Abstract: Systems, methods, and software to automatically detect print defects in printed matter. Some embodiments include receiving a reference image and a scanned image, wherein the reference image and the scanned image are of the same subject matter and generating a plurality of detail maps of the reference image and the scanned image at each one or more resolutions to derive one or more detail maps of each image at each resolution. The detail maps, in some embodiments, are generated by identifying differences between pixels in each of one or more directions. The embodiments further include dividing the detail maps of each image into blocks of equal image proportion and calculate an activity measure of each block in each of the detail maps. These embodiments further calculate similarity measures between the blocks of reference image detail maps and the respective blocks of the scanned image detail maps.

Abstract: Various embodiments of the present invention are directed to methods and systems for image processing that are unified in nature, carrying out many image-enhancement tasks together in a unified approach, rather than sequentially executing separately implemented, discrete image-enhancement tasks. In addition, the methods and systems of the present invention can apply image-enhancement to local, spatial regions of an image, rather than relying on global application of enhancement techniques that result in production of artifacts and distortions. In certain embodiments of the present invention, various different types of intermediate images are produced at each of a number of different scales from a received, input image.

Abstract: Embodiments of the present invention employ robust filtering at each of a number of scales, or resolutions, within a signal, such as a still image or video sequence. In certain embodiments of the present invention, robust filtering comprises or includes a non-linear neighborhood operation at each scale in order to produce both a denoised, sharpened and contrast-enhanced signal and a correction signal at each scale. Correction signals and sharpened and contrast-enhanced signals are combined to produce a denoised, sharpened and contrast-enhanced output signal. Scale-appropriate enhancements at each scale or resolution, including denoising, sharpening and contrast enhancement, ameliorate artifacts introduced by re-enhancement, at a second scale or resolution, features previously enhanced at another scale or resolution and inhibit over enhancement.

Abstract: Embodiments of the present invention employ robust filtering at each of a number of scales, or resolutions, within a signal, such as a still image or video sequence. In certain embodiments of the present invention, robust filtering comprises or includes a non-linear neighborhood operation at each scale in order to produce both a denoised, sharpened and contrast-enhanced signal and a correction signal at each scale. Correction signals and sharpened and contrast-enhanced signals are combined to produce a denoised, sharpened and contrast-enhanced output signal. Scale-appropriate enhancements at each scale or resolution, including denoising, sharpening and contrast enhancement, ameliorate artifacts introduced by re-enhancement, at a second scale or resolution, features previously enhanced at another scale or resolution and inhibit over enhancement.

Abstract: In one respect, provided are systems, methods and techniques in which local regions within an image are processed to provide fuzzy classification scores, which are calculated by determining changes in pixel values along a number of different directions. The resulting fuzzy classification scores are then used to detect or identify edge-containing or texture-containing regions, or to otherwise process the image regions differentially according to their fuzzy classification score. In another respect, provided are systems, methods and techniques for differential processing of different areas in an image. The differential processing in this case is based on calculated measures of local activity, which indicate features in corresponding local regions, and also based on calculated measures of local pixel-value variations, which indicate an amount of variation in pixel values across the corresponding local regions.

Abstract: Provided are systems, methods and techniques which use local image properties to determine which potential defects in an image should be corrected. In one representative embodiment, potential defects in an image are identified based on edge sharpness, and measures of strength for the different potential defects are calculated. The measures of strength for the potential defects are then evaluated in view of certain local properties of the image, in an immediate vicinity of such potential defects, in order to identify the image defects to be corrected.

Abstract: Provided are systems, methods and techniques that estimate the noise level in a signal, such as an image, by ordering windows in the signal based on calculated measures of the variability within each window (i.e., ordering from lowest to highest or, alternatively, from highest to lowest). That order information is then used together with the calculated measures of variability to form an estimate of the noise level. Typically, the techniques of the present invention generate this estimate based on the windows having the lowest or the second-lowest or the several lowest, depending upon the nature of the image, measures of variability.

Abstract: Processing of a digital image includes applying a function to a plurality of pixels in the image, the function relating a noise statistic to pixel intensity values. By applying the noise statistic function, a noise statistic value is produced for each of the pixels. Each of the pixels is processed with respect to its noise statistic value.

Abstract: Representations of an input signal at different lower resolutions are obtained, where n denotes a level of resolution, and where 0?n?N. Level n=N corresponds to the representation having the lowest resolution, and level n=0 corresponds to the input signal. The resolution increases as level n decreases. The nth level representation is used to estimate and remove low frequency error in the n?1th level representation. Then application-specific processing is performed on the n?1th level representation.

Abstract: Systems, methods, and software to automatically detect print defects in printed matter. Some embodiments include receiving a reference image and a scanned image, wherein the reference image and the scanned image are of the same subject matter and generating a plurality of detail maps of the reference image and the scanned image at each one or more resolutions to derive one or more detail maps of each image at each resolution. The detail maps, in some embodiments, are generated by identifying differences between pixels in each of one or more directions. The embodiments further include dividing the detail maps of each image into blocks of equal image proportion and calculate an activity measure of each block in each of the detail maps. These embodiments further calculate similarity measures between the blocks of reference image detail maps and the respective blocks of the scanned image detail maps.

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.