Fundamentals of digital image processing
Fundamentals of digital image processing
Wavelet analysis: the scalable structure of information
Wavelet analysis: the scalable structure of information
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
Modified Method for Denoising Color Images using Fuzzy Approach
SNPD '08 Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing
A new fuzzy-based wavelet shrinkage image denoising technique
ACIVS'06 Proceedings of the 8th international conference on Advanced Concepts For Intelligent Vision Systems
Noise reduction by fuzzy image filtering
IEEE Transactions on Fuzzy Systems
Analysis of multiresolution image denoising schemes using generalized Gaussian and complexity priors
IEEE Transactions on Information Theory
Wavelet thresholding via MDL for natural images
IEEE Transactions on Information Theory
De-noising by soft-thresholding
IEEE Transactions on Information Theory
Adaptive wavelet thresholding for image denoising and compression
IEEE Transactions on Image Processing
A joint inter- and intrascale statistical model for Bayesian wavelet based image denoising
IEEE Transactions on Image Processing
A Fuzzy Noise Reduction Method for Color Images
IEEE Transactions on Image Processing
Computers and Electrical Engineering
Salt and pepper noise filtering with fuzzy-cellular automata
Computers and Electrical Engineering
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The paper proposes a new fuzzy-based two-step filter for restoring images corrupted with additive noise. The goal of the first step is to compute the difference between the central pixel and its neighborhood in a selected window and to compute a fuzzy membership degree for each difference value using a Gaussian membership function. Computed fuzzy membership values are appropriately utilized as weights for each pixel and then computes the weighted average representing the modified value for the current central pixel. The second step is used as an augmented step to the first one and its goal is to improve the result obtained in the first step by reducing the noise in the color component differences without destroying the fine details of the image. The experimental analysis shows that the proposed method gives better results compared to existing advanced filters for additive noise reduction. Both visual, quantitative and qualitative analysis have been done to prove the efficiency and effectiveness of the proposed method.