Non-Bayer color filter array (CFA) sensors have recently drawn attention due to their superior compression of spectral energy, ability to deliver improved signal-to-noise ratio, or ability to provide high dynamic range (HDR) imaging. Demosaicking methods that perform color interpolation of Bayer CFA data have been widely investigated. However, a bottleneck to the adaption of emerging non-Bayer CFA sensors is the unavailability of efficient color-interpolation algorithms that can demosaick the new patterns. Designing a new demosaick algorithm for every proposed CFA pattern is a challenge. In this paper, we propose a hardware-friendly universal demosaick algorithm based on maximum a-posteriori (MAP) estimation that can be configured to demosaick raw images captured using a variety of CFA sensors. The forward process of mosaicking is modeled as a linear operation. We then use quadratic data-fitting and image prior terms in a MAP framework and pre-compute the inverse matrix for recovering the full RGB image from CFA observations for a given pattern. The pre-computed inverse is later used in real-time application to demosaick the given CFA pattern. The inverse matrix is observed to have a Toeplitz-like structure, allowing for hardware-efficient implementation of the algorithm. We use a set of 24 Kodak color images to evaluate the quality of our demosaick algorithm on three different CFA patterns. The PSNR values of the reconstructed full-channel RGB images from CFA samples are reported in the paper.

Typical digital cameras use a single-chip image sensor covered with a mosaic of red, green, and blue color filters for capturing color information. At each pixel location, only one of the three color values is known. The interpolation of the two missing color values at each pixel in a color filter array image (CFA) is called demosaicing.

Conventional electrophotographic printers tend to produce Moiré artifacts when used for printing images scanned from printed material such as books and magazines. We propose a novel noniterative, nonlinear, and space-variant descreening filter that removes a wide range of Moiré-causing screen frequencies in a scanned document while preserving image sharpness and edge detail. This filter is inspired by Perona-Malik's anisotropic diffusion equation. The amount of diffusion of the image intensity resulting from applying the filter is governed by an edge intensity estimate that is robust under halftone noise. More precisely, the filter extracts a spatial feature vector comprising local intensity gradients estimated from a local window in a presmoothed version of the noisy input image. Tunable nonlinear polynomial functions of this feature vector are then used to perform one iteration of a discrete diffusion controlled by the intensity gradient. The polynomial functions and feature extraction kernels are selected empirically in order to minimize computation while ensuring robust performance across a wide range of test images on a target imaging platform. The algorithm uses integer arithmetic, mostly relying on low-cost bit-wise shift and addition operations, and uses a strictly sequential architecture to provide a cost-effective and robust descreening solution in practical imaging devices including copiers and multifunction printers. We compare the performance of the proposed algorithm to other descreening solutions and demonstrate that the new algorithm improves quality over the existing methods while reducing computation.

Conventional electrophotographic printers tend to produce Moire artifacts when used for printing images scanned from printed material such as books and magazines. Inspired by anisotropic diffusion, we propose a novel non-iterative, non-linear, and space-variant de- screening filter that removes a wide range of Moire-causing screen frequencies in a scanned document while preserving image sharpness and edge detail. The amount of diffusion of the image intensity resulting from applying the filter is governed by an estimate of the gradient that is robust under halftone noise. More precisely, the filter extracts a spatial feature vector comprising local intensity gradients estimated from a local window in a pre-smoothed version of the noisy input image. Tunable non-linear polynomial functions of this feature vector are then used to perform one iteration of a discrete diffusion controlled by the intensity gradient. We compare the performance of the proposed algorithm to other descreening solutions and demonstrate that the new algorithm improves quality over the existing methods while reducing computation.

In this paper, we propose a hierarchical color correction algorithm for enhancing the color of digital images obtained from low-quality digital image capture devices such as cell phone cameras. The proposed method is based on a multilayer hierarchical stochastic framework whose parameters are learned in an offline training procedure using the well-known expectation maximization (EM) algorithm. This hierarchical framework functions by first making soft assignments of images into defect classes and then processing the images in each defect class with an optimized algorithm. The hierarchical color correction is performed in three stages. In the first stage, global color attributes of the low-quality input image are used in a Gaussian mixture model (GMM) framework to perform a soft classification of the image into M predefined global image classes. In the second stage, the input image is processed with a nonlinear color correction algorithm that is designed for each of the M global classes. This color correction algorithm, which we refer to as resolution synthesis color correction (RSCC), applies a spatially varying color correction determined by the local color attributes of the input image. In the third stage, the outputs of the RSCC predictors are combined using the global classification weights to yield the color corrected output image. We compare the performance of the proposed method to other commercial color correction algorithms on cell phone camera images obtained from different sources. Both subjective and objective measures of quality indicate that the new color correction algorithm improves quality over the existing methods.

In this paper, we propose a method for improving the color rendition of low quality cell phone camera images. The proposed method is based on a multi layer stochastic framework whose parameters are learned in an offline training procedure using the well known expectation maximization (EM) algorithm. The color correction algorithm functions by first making soft assignments of images into defect classes and then processing images in each defect class with an optimized algorithm, which we refer to as resolution synthesis-based color correction (RSCC). The parameters of the color correction algorithm are trained using pairs of low quality images, obtained from real cell phone cameras, and high quality spatially registered reference images, captured with a high quality digital still camera. We present experimental results comparing the performance of our method to some existing commercial color correction algorithms.

Conventional halftoning methods employed in electrophotographic printers tend to produce Moiré artifacts when used for printing images scanned from printed material, such as books and magazines. We present a novel approach for descreening color scanned documents aimed at providing an efficient solution to the Moiré problem in practical imaging devices, including copiers and multifunction printers. The algorithm works by combining two nonlinear image-processing techniques, resolution synthesis-based denoising (RSD), and modified smallest univalue segment assimilating nucleus (SUSAN) filtering. The RSD predictor is based on a stochastic image model whose parameters are optimized beforehand in a separate training procedure. Using the optimized parameters, RSD classifies the local window around the current pixel in the scanned image and applies filters optimized for the selected classes. The output of the RSD predictor is treated as a first-order estimate to the descreened image. The modified SUSAN filter uses the output of RSD for performing an edge-preserving smoothing on the raw scanned data and produces the final output of the descreening algorithm. Our method does not require any knowledge of the screening method, such as the screen frequency or dither matrix coefficients, that produced the printed original. The proposed scheme not only suppresses the Moiré artifacts, but, in addition, can be trained with intrinsic sharpening for deblurring scanned documents. Finally, once optimized for a periodic clustered-dot halftoning method, the same algorithm can be used to inverse halftone scanned images containing stochastic error diffusion halftone noise.