A new nonlinear iterative average filtering technique for removing random impulse noise from images is presented. It can be used for both grayscale and color images. Filtering results of the standard test images and comparisons with results of other denoising methods are presented. The authors' approach offers especially good results for strongly corrupted images (corruption pixel ratio above 40%).

Iterative average filtering for image denoising

In this paper, the root-cause for the appearance of various noises in the image and video are described. A hardware implementation of the median filter using Dynamic Partial Reconfiguration (DPR) technique to restore grayscale and color image having salt & peppers and random-valued impulse noise is proposed. The performance of proposed pipelined-parallel DPR architecture is validated in terms of different quantitative metrics and the experimental results are analyzed.

Digital filter for real-time impulse noise suppression in video processing using Dynamic Partial Reconfiguration technique

Image restoration is defined as a technique to bring back contents of initial image from the degraded one with or without having prior knowledge about degradation process.Image degradation may appear in any of the processing phases including: acquisition, reproduction, storage, transmission, compression, and/or pre-processing phase.In this thesis, research has been presented in the domain of image restoration. Image restoration offers exciting application and research opportunities for many application domains e.g. astronomical imaging, medical imaging, defense applications along with numerous other post-processing techniques.A degradation model needs to be developed first for image restoration.This degradation is usually a consequence of addition of blur or noise. Once the degradation has been modeled, images can be restored towards the approximation of their reality.However, in reality, a-priori information about the blur or noise is not known in many situations thereby making image restoration a more difficult task.In order to overcome this problem, estimation techniques are used on true image as well as degraded one to get a better approximation.Major focus of this thesis has been to study existing modern soft computing based techniques and to develop new image restoration techniques using soft computing.Research centers around restoration of corrupted images subjected to different types of impulse noise for grayscale as well as for color images.This restoration is achieved by making good tradeoff between two essential but contradictory characteristics of images i.e. smoothness and edge preservation. This dissertation makes the following contributions in the field of image restoration: (1) Machine Learning based Impulse Noise Detector is proposed which has high noise detection accuracy and very few false alarms for whole range of noise density. (2) Directional Weighted Switching Median filter is proposed which performs well even at high noise density and has outstanding detail preservation capability.(3) Fuzzy based filter using statistical estimators is proposed for random-valued impulse noise. (4) Color differences based fuzzy color filter is proposed which preserves the color differences of the image. (5) In the end, a novel technique is proposed for noise type identification making automatic image restoration techniques to be more generic and useful.Significant experimentation is performed to evaluate performance of proposed techniques with the impressive results.This experimentation is based upon both qualitative and quantitative error measures

Image Restoration using Soft Computing

Iteracyjna metoda usuwania szumu impulsowego z obrazów cyfrowych

Abstract In this modern era, visual data transmission, processing, and analysis play a vital role in daily life. Image denoising is the process of approximately estimating the original version of a degraded image. The presence of unexpected noise (e.g., fixed, random, and Gaussian) is the root cause of degradation, which has been reduced to some extent by many linear and non-linear filters based on a median value. The real issue is developing a strategy that should be generalized enough to effectively restore an image corrupted with multi-nature noise. Many researchers have developed novel concepts, but their tactics must acquire the highest performance in this area. This article proposes a constrained strategy for this problem, i.e., an adaptively directed denoising filter (ADD filter) based on a neural network. It consists of three major stages: training, filtering, and enhancing. First, we train a feed-forward back-propagation neural network on noisy and noise-free pixels for effective differentiation. Second, we apply a one-pass selective filter to the noisy image. The objective of this one-pass filter is to minimize noise using an adaptive median or directional filter based on density. Finally, the iterative directional filter is applied to the pre-processed image to enhance its visual quality. The extensive experiments depict that the proposed system has achieved better subjective results and improved local (structural similarity) and global (peak signal-to-noise ratio or mean square error) statistical measures. 

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Adaptively Directed Image Restoration Using Resilient Backpropagation Neural Network

In this paper, a two-pass filter is proposed for removing impulse noise to improve image quality. Due to the fact that the edge is more obvious and detected easily, such characteristics of different edges are used to judge whether the pixel is on an edge. Then, the pixels are not on the edges will be filtered by median filter, and further, the other pixels will be detected to preserve original information and reduce impulse noise. Experimental results show that the proposed method is more effective for impulse noise reduction to improve image quality; especially the corruption ratio above 20%.

Yi-Li Kuo

Papers

A Two-Pass Filter for Impulse Noise Reduction Based on Edge Characteristics