Algorithm for Parallel Inverse Halftoning using Partitioning of Look-Up Table (LUT)

The Look-Up Table (LUT) method for inverse halftoning is fast and computation-free technique employed to obtain good quality images. In this work we propose a new algorithm to parallelize the LUT method so that more pixels can be concurrently inverse halftoned using minimum additional hardware. The proposed algorithm partitions the single LUT of serial LUT method into N smaller Look-Up Tables (s-LUTs) such that the total number of entries in all s-LUTs remain equal to the number of entries in the single LUT of serial LUT method. The proposed algorithm can be implemented on a single FPGA (Field Programmable Gate Arrays) device with external memories to store s-LUTs

LMS Adaptive Filters for Noise Cancellation: A Review

This paper reviews the past and the recent research on Adaptive Filter algorithms based on adaptive noise cancellation systems. In many applications of noise cancellation, the change in signal characteristics could be quite fast which requires the utilization of adaptive algorithms that converge rapidly. Algorithms such as LMS and RLS proves to be vital in the noise cancellation are reviewed including principle and recent modifications to increase the convergence rate and reduce the computational complexity for future implementation. The purpose of this paper is not only to discuss various noise cancellation LMS algorithms but also to provide the reader with an overview of the research conducted

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

Parallel Inverse Halftoning by Look-Up Table (LUT) Partitioning

Look-Up Table (LUT) method for inverse halftoning is computation less, fast, and also yields goods results. It employs a single LUT that is stored in a ROM and contains pre-computed contone (gray level) values for inverse halftone operation. This paper proposes an algorithm that can perform parallel inverse halftone operation by partitioning the single LUT into N smaller Look-Up Tables (s-LUTs). Thereby, upto k (k≤N) pixels can be concurrently fetched from the halftone image, and their contone values can also be fetched concurrently from separate smaller Look-Up Tables (s-LUT). The parallelization increases the speed of inverse halftoning by upto k times while the total entries in all s-LUTs remains equal to the entries in the single LUT of the serial LUT method. Some degradation in image quality is possible due to pixel loss during parallel fetching. This is due to some contone values cannot be fetched in the same cycle because some other contone value is being fetched from the s-LUT. The complete implementation of the algorithm requires two CPLD devices for computational portion, external content addressable memories (CAM) and static RAMs to store s-LUTs

Parallel Inverse Halftoning by Look-Up Table (LUT) Partitioning

Look-Up Table (LUT) method for inverse halftoning is computation less, fast, and also yields goods results. It employs a single LUT that is stored in a ROM and contains pre-computed contone (gray level) values for inverse halftone operation. This paper proposes an algorithm that can perform parallel inverse halftone operation by partitioning the single LUT into N smaller Look-Up Tables (s-LUTs). Thereby, upto k (k≤N) pixels can be concurrently fetched from the halftone image, and their contone values can also be fetched concurrently from separate smaller Look-Up Tables (s-LUT). The parallelization increases the speed of inverse halftoning by upto k times while the total entries in all s-LUTs remains equal to the entries in the single LUT of the serial LUT method. Some degradation in image quality is possible due to pixel loss during parallel fetching. This is due to some contone values cannot be fetched in the same cycle because some other contone value is being fetched from the s-LUT. The complete implementation of the algorithm requires two CPLD devices for computational portion, external content addressable memories (CAM) and static RAMs to store s-LUTs

Parallel Inverse Halftoning by Look-Up Table (LUT) Partitioning

Look-Up Table (LUT) method for inverse halftoning is computation less, fast, and also yields goods results. It employs a single LUT that is stored in a ROM and contains pre-computed contone (gray level) values for inverse halftone operation. This paper proposes an algorithm that can perform parallel inverse halftone operation by partitioning the single LUT into N smaller Look-Up Tables (s-LUTs). Thereby, upto k (k≤N) pixels can be concurrently fetched from the halftone image, and their contone values can also be fetched concurrently from separate smaller Look-Up Tables (s-LUT). The parallelization increases the speed of inverse halftoning by upto k times while the total entries in all s-LUTs remains equal to the entries in the single LUT of the serial LUT method. Some degradation in image quality is possible due to pixel loss during parallel fetching. This is due to some contone values cannot be fetched in the same cycle because some other contone value is being fetched from the s-LUT. The complete implementation of the algorithm requires two CPLD devices for computational portion, external content addressable memories (CAM) and static RAMs to store s-LUTs

Parallel Inverse Halftoning by Look-Up Table (LUT) Partitioning

Look-Up Table (LUT) method for inverse halftoning is computation less, fast, and also yields goods results. It employs a single LUT that is stored in a ROM and contains pre-computed contone (gray level) values for inverse halftone operation. This paper proposes an algorithm that can perform parallel inverse halftone operation by partitioning the single LUT into N smaller Look-Up Tables (s-LUTs). Thereby, upto k (k≤N) pixels can be concurrently fetched from the halftone image, and their contone values can also be fetched concurrently from separate smaller Look-Up Tables (s-LUT). The parallelization increases the speed of inverse halftoning by upto k times while the total entries in all s-LUTs remains equal to the entries in the single LUT of the serial LUT method. Some degradation in image quality is possible due to pixel loss during parallel fetching. This is due to some contone values cannot be fetched in the same cycle because some other contone value is being fetched from the s-LUT. The complete implementation of the algorithm requires two CPLD devices for computational portion, external content addressable memories (CAM) and static RAMs to store s-LUTs

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