Backward Diffusion Methods for Digital Halftoning

We examine using discrete backward diffusion to produce digital halftones. The noise introduced by the discrete approximation to backwards diffusion forces the intensity away from uniform values, so that rounding each pixel to black or white can produce a pleasing halftone. We formulate our method by considering the Human Visual System norm and approximating the inverse of the blurring operator. We also investigate several possible mobility functions for use in a nonlinear backward diffusion equation for higher quality results

Digital Color Imaging

This paper surveys current technology and research in the area of digital color imaging. In order to establish the background and lay down terminology, fundamental concepts of color perception and measurement are first presented us-ing vector-space notation and terminology. Present-day color recording and reproduction systems are reviewed along with the common mathematical models used for representing these devices. Algorithms for processing color images for display and communication are surveyed, and a forecast of research trends is attempted. An extensive bibliography is provided

Structure-aware halftoning

our result faithfully preserves the texture details as well as the local tone. All images have the same resolution of 445×377. This paper presents an optimization-based halftoning technique that preserves the structure and tone similarities between the original and the halftone images. By optimizing an objective function consisting of both the structure and the tone metrics, the generated halftone images preserve visually sensitive texture details as well as the local tone. It possesses the blue-noise property and does not introduce annoying patterns. Unlike the existing edge-enhancement halftoning, the proposed method does not suffer from the deficiencies of edge detector. Our method is tested on various types of images. In multiple experiments and the user study, our method consistently obtains the best scores among all tested methods.

Minimization of Halftone Noise in FLAT Regions for Improved Print Quality

The work in this thesis proposes a novel algorithm for enhancing the quality of flat regions in printed color image documents. The algorithm is designed to identify the flat regions based on certain criteria and filter these regions to minimize the noise prior and post Halftoning so as to make the hard copy look visibly pleasing. Noise prior to halftone process is removed using a spatial Gaussian filter together with a Hamming window, concluded from results after implementing various filtering techniques. A clustered dithering is applied in each channel of the image as Halftoning process. Furthermore, to minimize the post halftone noise, the halftone structure of the image is manipulated according to the neighboring sub-cells in their respective channels. This is done to reduce the brightness variation (a cause for noise) between the neighboring subcells. Experimental results show that the proposed algorithm efficiently minimizes noise in flat regions of mirumal gradient change in color images

Digital halftoning using fibonacci-like sequence pertubation and using vision-models in different color spaces

A disadvantage in error diffusion is that it creates objectionable texture patterns at certain gray levels. An approach, threshold perturbation by Fibonacci-like sequences, was studied. This process is simpler than applying a vision model and it also decreases the visible patterns in error diffusion. Vector error diffusion guarantees minimum color distance in binarization provided that a uniform color space is used. Four color spaces were studied in this research. It was found that vector error diffusion in two linear color spaces made no reduction in the quality of halftoning compared with that in CIEL*a*b* or CIEL*u*v* color spaces. A luminance vision MTF and a chroma vision MTF were used in model-based error diffusion to further improve the halftone image quality

Low-complexity high-performance multiscale error diffusion technique for digital halftoning

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Media processor implementations of image rendering algorithms

Demands for fast execution of image processing are a driving force for today\u27s computing market. Many image processing applications require intense numeric calculations to be done on large sets of data with minimal overhead time. To meet this challenge, several approaches have been used. Custom-designed hardware devices are very fast implementations used in many systems today. However, these devices are very expensive and inflexible. General purpose computers with enhanced multimedia instructions offer much greater flexibility but process data at a much slower rate than the custom-hardware devices. Digital signal processors (DSP\u27s) and media processors, such as the MAP-CA created by Equator Technologies, Inc., may be an efficient alternative that provides a low-cost combination of speed and flexibility. Today, DSP\u27s and media processors are commonly used in image and video encoding and decoding, including JPEG and MPEG processing techniques. Little work has been done to determine how well these processors can perform other image process ing techniques, specifically image rendering for printing. This project explores various image rendering algorithms and the performance achieved by running them on a me dia processor to determine if this type of processor is a viable competitor in the image rendering domain. Performance measurements obtained when implementing rendering algorithms on the MAP-CA show that a 4.1 speedup can be achieved with neighborhood-type processes, while point-type processes achieve an average speedup of 21.7 as compared to general purpose processor implementations

Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and visual quality improvement. However, in inverse halftoning, homogenous dot patterns hinder a small output range from the residual layers. Therefore, a new layer decomposition network based on the Gaussian convolution model (GCM) and structure-aware deblurring strategy is presented to achieve residual learning for both the base and detail layers. For the base layer, a new GCM-based residual subnetwork is presented. The GCM utilizes a statistical distribution, in which the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter can result in a narrow output range. Subsequently, the GCM-based residual subnetwork uses a Gaussian-filtered halftoned image as input and outputs the image difference as residual, thereby generating the base layer, i.e., the Gaussian-blurred continuous-tone image. For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of the base layer, the SARDS uses the predicted base layer as input and outputs the deblurred version. To more effectively restore image structures such as lines and texts, a new image structure map predictor is incorporated into the deblurring network to induce structure-adaptive learning. This paper provides a method to realize the residual learning of both the base and detail layers based on the GCM and SARDS. In addition, it is verified that the proposed method surpasses state-of-the-art methods based on U-Net, direct deblurring networks, and progressively residual networks