Abstract: Digital imaging systems, articles of manufacture, and digital image processing methods are described. According to one aspect, a digital imaging system includes an imaging system configured to provide image data of an image, the image data comprising digital image data for a plurality of pixel locations and processing circuitry configured to process the image data provided by the imaging system to denoise and sharpen the image data, wherein the processing circuitry, for an individual one of the pixel locations, is configured to identify a respective subset of the image data corresponding to the one pixel location and perform a single processing operation using the image data of the identified subset of the image data to denoise and sharpen the image data of the individual one pixel location.

Abstract: A neural network is trained with input data. The neural network is used to rescale the input data. Errors for the rescaled values are determined, and neighborhoods of the errors are used adjust connection weights of the neural network.

Abstract: A scanning device includes a scanning mechanism and a processing mechanism. The scanning mechanism scans an image fixed on a medium to generate a digital infrared representation of the image and a digital visible light representation of the image. The processing mechanism substantially reduces effects of noise and distortions within the digital visible light representation of the image in one pass. The processing mechanism at least decorrelates visible light aspects from the infrared representation of the image and employs a one-pass filter that uses both the infrared and the visible light representations of the image.

Abstract: A method for processing an image using a bilateral filter. The bilateral filter is reformulated at each pixel location in the image into a sum of the original signal value of a central pixel at said pixel location and a bilateral correction term which is a function of local signal differences between the central pixel and its neighbors. The bilateral correction term is calculated using a computationally efficient approximation.

Abstract: A method for processing an image using a bilateral filter. The bilateral filter is reformulated at each pixel location in the image into a sum of the original signal value of a central pixel at said pixel location and a bilateral correction term which is a function of local signal differences between the central pixel and its neighbors. The bilateral correction term is calculated using a computationally efficient approximation.

Abstract: Noise level estimation includes examining color correlation in a digital image.

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: Denoising of a plurality of pixels of a color digital image includes analyzing the plurality of pixels to identify those pixels having a high likelihood of containing significant noise; and forcing those pixels having a high likelihood of containing significant noise to be photometrically coherent with nearby pixels having a lower likelihood of containing significant noise.

Abstract: Image sharpening is performed by applying variable contrast stretching to pixels of interest in a digital image. For each pixel of interest, the amount of contrast stretching is a function of minimum and maximum intensity values in a local pixel neighborhood.

Abstract: A system and method of image processing for smoothing, denoising, despeckling and sharpening scanned document images which is performed prior to a compression. The scanned image is selectively smoothed by anisotropic diffusion filtering in a single iteration with a 3×3 kernel, which provides denoising, edge-preserving smoothing. The smoothed image data is then selectively sharpened using variable contrast mapping that provides overshoot-free variable-sharpening and despeckling. Image quality is improved while increasing compressibility of the image.

Abstract: Digital imaging systems, articles of manufacture, and digital image processing methods are described. According to one aspect, a digital imaging system includes an imaging system configured to provide image data of an image, the image data comprising digital image data for a plurality of pixel locations and processing circuitry configured to process the image data provided by the imaging system to denoise and sharpen the image data, wherein the processing circuitry, for an individual one of the pixel locations, is configured to identify a respective subset of the image data corresponding to the one pixel location and perform a single processing operation using the image data of the identified subset of the image data to denoise and sharpen the image data of the individual one pixel location.

Abstract: A method for processing an image using a bilateral filter. The bilateral filter is reformulated at each pixel location in the image into a sum of the original signal value of a central pixel at said pixel location and a bilateral correction term which is a function of local signal differences between the central pixel and its neighbors. The bilateral correction term is calculated using a computationally efficient approximation.

Abstract: Digital image processing methods, digital image devices and articles of manufacture are described. According to one aspect, a digital image processing method includes providing digital image data of a plurality of colors of an image, wherein the image data comprises a plurality of sets individually comprising mosaic data of one of a plurality of colors at a plurality of pixel locations, analyzing image data of one of the pixel locations with respect to image data of another of the pixel locations, and adjusting the image data of the one pixel location responsive to the analyzing, wherein the adjusting comprises adjusting to one of denoise the image data and sharpen the image data.

Abstract: A neural network is trained with input data. The neural network is used to rescale the input data. Errors for the rescaled values are determined, and neighborhoods of the errors are used adjust connection weights of the neural network.