Bayes-optimal inverse halftoning and statistical mechanics of the Q-Ising model

On the basis of statistical mechanics of the Q-Ising model, we formulate the Bayesian inference to the problem of inverse halftoning, which is the inverse process of representing gray-scales in images by means of black and white dots. Using Monte Carlo simulations, we investigate statistical properties of the inverse process, especially, we reveal the condition of the Bayes-optimal solution for which the mean-square error takes its minimum. The numerical result is qualitatively confirmed by analysis of the infinite-range model. As demonstrations of our approach, we apply the method to retrieve a grayscale image, such as standard image `Lenna', from the halftoned version. We find that the Bayes-optimal solution gives a fine restored grayscale image which is very close to the original.Comment: 13pages, 12figures, using elsart.cl

A Parallel Algorithm for Inverse Halftoning and its Hardware

Lookup Table (LUT) method for inverse halftoning is computation less, fast and also yields goods results. This paper proposes a parallel algorithm for inverse halftoning by parallelizing the LUT method of inverse halftoning. The LUT method for inverse halftoning is parallelized by dividing the single Look-Up Table of LUT method for inverse halftoning into many smaller Look-up Tables (sLUTs). In the parallel algorithm up-to four pixels can be fetched from the halftone image concurrently and go to their separate smaller Look-Up Tables (sLUT) from where each template fetches its inverse halftone value independent to other pixels. The parallelization can increase the speed of inverse halftoning by up-to 4 times while the total entries in all smaller Look-Up Tables (sLUTs) remains equal to the entries in the single LUT of LUT method for inverse halftoning. Some degradation in image quality is noticed due to parallelization. The complete implementation of the method takes two CPLD devices with external content addressable memories (CAM) and static RAMs to store sLUTs

A Parallel Algorithm for Inverse Halftoning and its Hardware Implementation

Abstract Lookup Table (LUT) method for inverse halftoning is computation less, fast and also yields goods results. This paper proposes a parallel algorithm for inverse halftoning by parallelizing the LUT method of inverse halftoning. The LUT method for inverse halftoning is parallelized by dividing the single Look-Up Table of LUT method for inverse halftoning into many smaller Look-up Tables (sLUTs). In the parallel algorithm up-to four pixels can be fetched from the halftone image concurrently and go to their separate smaller Look-Up Tables (sLUT) from where each template fetches its inverse halftone value independent to other pixels. The parallelization can increase the speed of inverse halftoning by up-to 4 times while the total entries in all smaller Look-Up Tables (sLUTs) remains equal to the entries in the single LUT of LUT method for inverse halftoning. Some degradation in image quality is noticed due to parallelization. The complete implementation of the method takes two CPLD devices with external content addressable memories (CAM) and static RAMs to store sLUTs. Keywords: (1) Inverse Halftoning (2) Hardware Implementation (3) Look-Up Table Inverse Halftoning (4) Complex Programmable Logic Devices (CPLD) (5) Image Processin

A Parallel Algorithm for Inverse Halftoning and its Hardware Implementation

Abstract Lookup Table (LUT) method for inverse halftoning is computation less, fast and also yields goods results. This paper proposes a parallel algorithm for inverse halftoning by parallelizing the LUT method of inverse halftoning. The LUT method for inverse halftoning is parallelized by dividing the single Look-Up Table of LUT method for inverse halftoning into many smaller Look-up Tables (sLUTs). In the parallel algorithm up-to four pixels can be fetched from the halftone image concurrently and go to their separate smaller Look-Up Tables (sLUT) from where each template fetches its inverse halftone value independent to other pixels. The parallelization can increase the speed of inverse halftoning by up-to 4 times while the total entries in all smaller Look-Up Tables (sLUTs) remains equal to the entries in the single LUT of LUT method for inverse halftoning. Some degradation in image quality is noticed due to parallelization. The complete implementation of the method takes two CPLD devices with external content addressable memories (CAM) and static RAMs to store sLUTs. Keywords: (1) Inverse Halftoning (2) Hardware Implementation (3) Look-Up Table Inverse Halftoning (4) Complex Programmable Logic Devices (CPLD) (5) Image Processin

Task-Driven Dictionary Learning

Modeling data with linear combinations of a few elements from a learned dictionary has been the focus of much recent research in machine learning, neuroscience and signal processing. For signals such as natural images that admit such sparse representations, it is now well established that these models are well suited to restoration tasks. In this context, learning the dictionary amounts to solving a large-scale matrix factorization problem, which can be done efficiently with classical optimization tools. The same approach has also been used for learning features from data for other purposes, e.g., image classification, but tuning the dictionary in a supervised way for these tasks has proven to be more difficult. In this paper, we present a general formulation for supervised dictionary learning adapted to a wide variety of tasks, and present an efficient algorithm for solving the corresponding optimization problem. Experiments on handwritten digit classification, digital art identification, nonlinear inverse image problems, and compressed sensing demonstrate that our approach is effective in large-scale settings, and is well suited to supervised and semi-supervised classification, as well as regression tasks for data that admit sparse representations.Comment: final draft post-refereein