Computer and Robot Vision Vol. 1, by R.M. Haralick and Linda G. Shapiro, Addison-Wesley, 1992, ISBN 0-201-10887-1.

Computer and Robot Vision

Mixed noise removal from natural images is a challenging task since the noise distribution usually does not have a parametric model and has a heavy tail. One typical kind of mixed noise is additive white Gaussian noise (AWGN) coupled with impulse noise (IN). Many mixed noise removal methods are detection based methods. They first detect the locations of IN pixels and then remove the mixed noise. However, such methods tend to generate many artifacts when the mixed noise is strong. In this paper, we propose a simple yet effective method, namely weighted encoding with sparse nonlocal regularization (WESNR), for mixed noise removal. In WESNR, there is not an explicit step of impulse pixel detection; instead, soft impulse pixel detection via weighted encoding is used to deal with IN and AWGN simultaneously. Meanwhile, the image sparsity prior and nonlocal self-similarity prior are integrated into a regularization term and introduced into the variational encoding framework. Experimental results show that the proposed WESNR method achieves leading mixed noise removal performance in terms of both quantitative measures and visual quality.

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Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization

This paper presents a new fuzzy switching median (FSM) filter employing fuzzy techniques in image processing. The proposed filter is able to remove salt-and-pepper noise in digital images while preserving image details and textures very well. By incorporating fuzzy reasoning in correcting the detected noisy pixel, the low complexity FSM filter is able to outperform some well known existing salt-and-pepper noise fuzzy and classical filters.

Salt-and-pepper noise detection and reduction using fuzzy switching median filter

In this paper, we propose a switching bilateral filter (SBF) with a texture and noise detector for universal noise removal. Operation was carried out in two stages: detection followed by filtering. For detection, we propose the sorted quadrant median vector (SQMV) scheme, which includes important features such as edge or texture information. This information is utilized to allocate a reference median from SQMV, which is in turn compared with a current pixel to classify it as impulse noise, Gaussian noise, or noise-free. The SBF removes both Gaussian and impulse noise without adding another weighting function. The range filter inside the bilateral filter switches between the Gaussian and impulse modes depending upon the noise classification result. Simulation results show that our noise detector has a high noise detection rate as well as a high classification rate for salt-and-pepper, uniform impulse noise and mixed impulse noise. Unlike most other impulse noise filters, the proposed SBF achieves high peak signal-to-noise ratio and great image quality by efficiently removing both types of mixed noise, salt-and-pepper with uniform noise and salt-and-pepper with Gaussian noise. In addition, the computational complexity of SBF is significantly less than that of other mixed noise filters.

Switching Bilateral Filter With a Texture/Noise Detector for Universal Noise Removal

A new switching-based median filtering scheme for restoration of images that are highly corrupted by salt and pepper noise is proposed. An algorithm based on the scheme is developed. The new scheme introduces the concept of substitution of noisy pixels by linear prediction prior to estimation. A novel simplified linear predictor is developed for this purpose. The objective of the scheme and algorithm is the removal of high-density salt and pepper noise in images. The new algorithm shows significantly better image quality with good PSNR, reduced MSE, good edge preservation, and reduced streaking. The good performance is achieved with reduced computational complexity. A comparison of the performance is made with several existing algorithms in terms of visual and quantitative results. The performance of the proposed scheme and algorithm is demonstrated.

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A new switching-based median filtering scheme and algorithm for removal of high-density salt and pepper noise in images

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Analysis of bifurcations of limit cycles with Lyapunov exponents and numerical normal forms

This paper presents an interpolation technique “mmINTone” for binary images, such as logos, diagrams, graphs and cartoons. The technique removes the jaggies from a pixel-replicated image, using concepts from mathematical morphology. As a result, the edges are smoother and jaggy-free. In this paper we describe the algorithm, show some results and compare it to existing interpolation methods as well as to a previously developed technique of ours, also based on mathematical morphology. We can conclude that mmINTone is considerably faster and similar in quality.

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Binary image interpolation based on mathematical morphology

In this paper we discuss an extensive comparative study of 38 different classical and fuzzy filters for noise reduction, both for impulse noise and gaussian noise. The goal of this study is twofold: (1) we want to select the filters that have a very good performance for a specific noise type of a specific strength; (2) we want to find out whether fuzzy filters offer an added value, i.e. whether fuzzy filters outperform classical filters. The first aspect is relevant since large comparative studies did not appear in the literature so far; the second aspect is relevant in the context of the use of fuzzy techniques in image processing in general.

Do fuzzy techniques offer an added value for noise reduction in images

https://biblio.ugent.be/publication/1097584/file/1098297.pdf

Convergence analysis of a numerical method to solve the adjoint linearized periodic orbit equations

We constructed several new fuzzy similarity measures for greyscale images that outperform the classical measures of comparison, like root mean square error or peak signal to noise ratio, in the sense of image quality evaluation. In this paper we investigate the usefulness of similarity measures for the comparison of colour images. Instead of applying the similarity measures for greyscale images component-wise, we extend the similarity measures to colour images by applying vector morphological operators. We restrict ourselves to an investigation in the RGB colour space

V. De Witte

Papers

Analysis of bifurcations of limit cycles with Lyapunov exponents and numerical normal forms

Binary image interpolation based on mathematical morphology

Do fuzzy techniques offer an added value for noise reduction in images

Convergence analysis of a numerical method to solve the adjoint linearized periodic orbit equations

Colour Image Comparison Using Vector Operators