An enhanced decision based Unsymmetric Trimmed Median Filter for removal of high density salt and papper noise
01 Dec 2015-pp 1-4
TL;DR: The proposed algoritm identifies a pixel as noisy if its intensity value is 0 or 255 and processes it using pixels in a 3×3 window and shows significantly better performance, particularly at high noise density, as compared to various methods reported in literature.
Abstract: This paper proposes a new algorithm for restoration of gray scale images corrupted by salt and pepper noise(SPN). The proposed algoritm identifies a pixel as noisy if its intensity value is 0 or 255 and processes it using pixels in a 3×3 window. If the window consists of noisy and non-noisy pixels, then the pixel to be processed is replaced with the trimmed median value of the non-noisy pixels. However, if only noisy pixels are there in the window then their mean value is used to process the pixel. The proposed method uses processed (i.e. the de-noised) pixels in the window while processing the noisy pixels and shows significantly better performance, particularly at high noise density, as compared to various methods reported in literature. Experimental results show improvements both visually and quantitatively compared to other reported methods.
Citations
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01 Dec 2017
TL;DR: The result of this study has shown that the proposed filter can remove Salt and Pepper noise efficiently at different density and restored the originality of the pixel.
Abstract: Noise removal in an image is important in image processing because noise can bring a great deal amount of problems such as degraded the quality image and loss of crucial information in image. Therefore, image filtering technique has been introduced to overcome the problems. One of the common noises in image is Salt and Pepper noise. A lot of studies have been conducted by researchers to remove this noise efficiently. However, most of the studies have difficulties in removing this noise especially at high density. Therefore, this research is conducted to improve image filtering technique and preserve the originality of the pixel. The proposed technique is developed based on Decision Based Algorithm and the performance is compared with Median Filter, Hybrid Median Filter, Adaptive Median Filter and Ben Filter. Three images are used which are the lead frame, fruit and baboon images and the metric of comparison is determined by using Mean Square Error method. The result of this study has shown that the proposed filter can remove Salt and Pepper noise efficiently at different density and restored the originality of the pixel.
4 citations
References
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Book•
01 Jan 2000
TL;DR: The Handbook of Image and Video Processing contains a comprehensive and highly accessible presentation of all essential mathematics, techniques, and algorithms for every type of image and video processing used by scientists and engineers.
Abstract: 1.0 INTRODUCTION 1.1 Introduction to Image and Video Processing (Bovik) 2.0 BASIC IMAGE PROCESSING TECHNIQUES 2.1 Basic Gray-Level Image Processing (Bovik) 2.2 Basic Binary Image Processing (Desai/Bovik) 2.3 Basic Image Fourier Analysis and Convolution (Bovik) 3.0 IMAGE AND VIDEO PROCESSING Image and Video Enhancement and Restoration 3.1 Basic Linear Filtering for Image Enhancement (Acton/Bovik) 3.2 Nonlinear Filtering for Image Enhancement (Arce) 3.3 Morphological Filtering for Image Enhancement and Detection (Maragos) 3.4 Wavelet Denoising for Image Enhancement (Wei) 3.5 Basic Methods for Image Restoration and Identification (Biemond) 3.6 Regularization for Image Restoration and Reconstruction (Karl) 3.7 Multi-Channel Image Recovery (Galatsanos) 3.8 Multi-Frame Image Restoration (Schulz) 3.9 Iterative Image Restoration (Katsaggelos) 3.10 Motion Detection and Estimation (Konrad) 3.11 Video Enhancement and Restoration (Lagendijk) Reconstruction from Multiple Images 3.12 3-D Shape Reconstruction from Multiple Views (Aggarwal) 3.13 Image Stabilization and Mosaicking (Chellappa) 4.0 IMAGE AND VIDEO ANALYSIS Image Representations and Image Models 4.1 Computational Models of Early Human Vision (Cormack) 4.2 Multiscale Image Decomposition and Wavelets (Moulin) 4.3 Random Field Models (Zhang) 4.4 Modulation Models (Havlicek) 4.5 Image Noise Models (Boncelet) 4.6 Color and Multispectral Representations (Trussell) Image and Video Classification and Segmentation 4.7 Statistical Methods (Lakshmanan) 4.8 Multi-Band Techniques for Texture Classification and Segmentation (Manjunath) 4.9 Video Segmentation (Tekalp) 4.10 Adaptive and Neural Methods for Image Segmentation (Ghosh) Edge and Boundary Detection in Images 4.11 Gradient and Laplacian-Type Edge Detectors (Rodriguez) 4.12 Diffusion-Based Edge Detectors (Acton) Algorithms for Image Processing 4.13 Software for Image and Video Processing (Evans) 5.0 IMAGE COMPRESSION 5.1 Lossless Coding (Karam) 5.2 Block Truncation Coding (Delp) 5.3 Vector Quantization (Smith) 5.4 Wavelet Image Compression (Ramchandran) 5.5 The JPEG Lossy Standard (Ansari) 5.6 The JPEG Lossless Standard (Memon) 5.7 Multispectral Image Coding (Bouman) 6.0 VIDEO COMPRESSION 6.1 Basic Concepts and Techniques of Video Coding (Barnett/Bovik) 6.2 Spatiotemporal Subband/Wavelet Video Compression (Woods) 6.3 Object-Based Video Coding (Kunt) 6.4 MPEG-I and MPEG-II Video Standards (Ming-Ting Sun) 6.5 Emerging MPEG Standards: MPEG-IV and MPEG-VII (Kossentini) 7.0 IMAGE AND VIDEO ACQUISITION 7.1 Image Scanning, Sampling, and Interpolation (Allebach) 7.2 Video Sampling and Interpolation (Dubois) 8.0 IMAGE AND VIDEO RENDERING AND ASSESSMENT 8.1 Image Quantization, Halftoning, and Printing (Wong) 8.2 Perceptual Criteria for Image Quality Evaluation (Pappas) 9.0 IMAGE AND VIDEO STORAGE, RETRIEVAL AND COMMUNICATION 9.1 Image and Video Indexing and Retrieval (Tsuhan Chen) 9.2 A Unified Framework for Video Browsing and Retrieval (Huang) 9.3 Image and Video Communication Networks (Schonfeld) 9.4 Image Watermarking (Pitas) 10.0 APPLICATIONS OF IMAGE PROCESSING 10.1 Synthetic Aperture Radar Imaging (Goodman/Carrera) 10.2 Computed Tomography (Leahy) 10.3 Cardiac Imaging (Higgins) 10.4 Computer-Aided Detection for Screening Mammography (Bowyer) 10.5 Fingerprint Classification and Matching (Jain) 10.6 Probabilistic Models for Face Recognition (Pentland/Moghaddam) 10.7 Confocal Microscopy (Merchant/Bartels) 10.8 Automatic Target Recognition (Miller) Index
1,678 citations
"An enhanced decision based Unsymmet..." refers background in this paper
...I. INTRODUCTION Images are often corrupted by impulse noise during their acquisition by camera sensors and during transmission through communication channels, or due to transmission errors, faulty memory locations [1] or timing errors in analog-to-digital conversion....
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TL;DR: Based on two types of image models corrupted by impulse noise, two new algorithms for adaptive median filters are proposed that have variable window size for removal of impulses while preserving sharpness and are superior to standard median filters.
Abstract: Based on two types of image models corrupted by impulse noise, we propose two new algorithms for adaptive median filters. They have variable window size for removal of impulses while preserving sharpness. The first one, called the ranked-order based adaptive median filter (RAMF), is based on a test for the presence of impulses in the center pixel itself followed by a test for the presence of residual impulses in the median filter output. The second one, called the impulse size based adaptive median filter (SAMF), is based on the detection of the size of the impulse noise. It is shown that the RAMF is superior to the nonlinear mean L/sub p/ filter in removing positive and negative impulses while simultaneously preserving sharpness; the SAMF is superior to Lin's (1988) adaptive scheme because it is simpler with better performance in removing the high density impulsive noise as well as nonimpulsive noise and in preserving the fine details. Simulations on standard images confirm that these algorithms are superior to standard median filters. >
1,172 citations
"An enhanced decision based Unsymmet..." refers background in this paper
...These filters process a given image into two steps: 1) detection of noisy pixels and 2) replacing them with their estimated values....
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TL;DR: This scheme can remove salt-and-pepper-noise with a noise level as high as 90% and show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only.
Abstract: This paper proposes a two-phase scheme for removing salt-and-pepper impulse noise. In the first phase, an adaptive median filter is used to identify pixels which are likely to be contaminated by noise (noise candidates). In the second phase, the image is restored using a specialized regularization method that applies only to those selected noise candidates. In terms of edge preservation and noise suppression, our restored images show a significant improvement compared to those restored by using just nonlinear filters or regularization methods only. Our scheme can remove salt-and-pepper-noise with a noise level as high as 90%.
1,078 citations
"An enhanced decision based Unsymmet..." refers background in this paper
...As the median filters use pixels in a window for processing of noisy pixels, its performance is highly dependent on the window size....
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Book•
01 Jan 1997
TL;DR: In this article, statistical analysis and optimization of nonlinear filter methods based on order statistics Stack Filters Multistage and Hybrid Filters Discussion Exercises Bibliography Index Index.
Abstract: Nonlinear Signal Processing Signal Processing Model Signal and Noise Models Fundamental Problems in Noise Removal Algorithms Statistical Preliminaries Random Variables and Distributions Signal and Noise Models Estimation Some Useful Distributions 1001 Solutions Trimmed Mean Filters Other Trimmed Mean Filters L-Filters C-Filters (Ll-Filters) Weighted Median Filters Ranked-Order and Weighted Order Statistic Filters Multistage Median Filters Median Hybrid Filters Edge-Enhancing Selective Filters Rank Selection Filters M-Filters R-Filters Weighted Majority with Minimum Range Filters Nonlinear Mean Filters Stack Filters Generalizations of Stack Filters Morphological Filters Soft Morphological Filters Polynomial Filters Data-Dependent Filters Decision-Based Filters Iterative, Cascaded, and Recursive Filters Some Numerical Measures of Nonlinear Filters Discussion Statistical Analysis and Optimization of Nonlinear Filters Methods Based on Order Statistics Stack Filters Multistage and Hybrid Filters Discussion Exercises Bibliography Index
729 citations
"An enhanced decision based Unsymmet..." refers background in this paper
...The main aim of denoising is to remove the noise effectively while preserving the original image details....
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TL;DR: A new impulse noise detection technique for switching median filters is presented, which is based on the minimum absolute value of four convolutions obtained using one-dimensional Laplacian operators, and is directed toward improved line preservation.
Abstract: A new impulse noise detection technique for switching median filters is presented, which is based on the minimum absolute value of four convolutions obtained using one-dimensional Laplacian operators. Extensive simulations show that the proposed filter provides better performance than many of the existing switching median filters with comparable computational complexity. In particular, the proposed filter is directed toward improved line preservation.
688 citations