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

Survey on Securing Medical Image Transmission using Visual Cryptography Techniques

Visual cryptography scheme is a cryptographic technique which allows visual information text or image to be encrypted in such a way that the decryption can be performed by the human visual system and without the aid of computers. It encodes the secret image into shares of different patterns. Visual Cryptography is done on black and white image as well as on color image. This paper includes the literature survey regarding Visual Cryptography techniques for secure medical image transmission

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

Analysis of random halftone dithering using second order statistics

An analytical approach is proposed to explain the appearance of unwanted low frequency artifacts during the random dithering halftoning process. The solution uses a theorem which relates the correlation of the input gray level (continuous) signal to the correlation of the output (halftone) binary signal. The numerical solution of the above relationship suggests that: 1. Introduction of low frequency artifacts is inevitable. 2. The effect is enhanced for mean gray levels farther from mid-gray. 3. High frequency information in the input signal is attenuated more than low frequency information