Stefan Schulte

Bio: Stefan Schulte is an academic researcher from Ghent University. The author has contributed to research in topics: Gaussian noise & Salt-and-pepper noise. The author has an hindex of 8, co-authored 11 publications receiving 402 citations.

A fuzzy impulse noise detection and reduction method

Abstract: Removing or reducing impulse noise is a very active research area in image processing. In this paper we describe a new algorithm that is especially developed for reducing all kinds of impulse noise: fuzzy impulse noise detection and reduction method (FIDRM). It can also be applied to images having a mixture of impulse noise and other types of noise. The result is an image quasi without (or with very little) impulse noise so that other filters can be used afterwards. This nonlinear filtering technique contains two separated steps: an impulse noise detection step and a reduction step that preserves edge sharpness. Based on the concept of fuzzy gradient values, our detection method constructs a fuzzy set impulse noise. This fuzzy set is represented by a membership function that will be used by the filtering method, which is a fuzzy averaging of neighboring pixels. Experimental results show that FIDRM provides a significant improvement on other existing filters. FIDRM is not only very fast, but also very effective for reducing little as well as very high impulse noise.

Fuzzy Filters for Noise Reduction: The Case of Gaussian Noise

Abstract: Noise reduction is a well-known problem in image processing The reduction of noise in an image sometimes is as a goal itself, and sometimes is considered as a pre-processing step Besides the classical filters for noise reduction, quite a lot of fuzzy inspired filters have been proposed during the past years However, it is very difficult to judge the quality of this wide variety of filters For which noise types are they designed? How do they perform for those noise types? How do they perform compared to each other? Can we select filters that clearly outperform the others? Is there a difference between numerical and visual results? In this paper, we answer these questions for images that are corrupted with Gaussian noise

Vector morphological operators for colour images

Abstract: In this paper we extend the basic morphological operators dilation and erosion for grey-scale images based on the threshold approach, umbra approach and fuzzy set theory to colour images. This is realised by treating colours as vectors and defining a new vector ordering so that new colour morphological operators are presented. Here we only discuss colours represented in the RGB colour space. The colour space RGB becomes together with the new ordering and associated minimum and maximum operators a complete chain. All this can be extended to the colour spaces HSV and L*a*b*. Experimental results show that our method provides an improvement on the component-based approach of morphological operators applied to colour images. The colours in the colour images are preserved, that is, no new colours are introduced.

A survey on the use and the construction of fuzzy similarity measures in image processing

Abstract: Fuzzy techniques can be applied in several domains of image processing. In this paper we will give a survey on how fuzzy similarity measures can be used in establishing measures for image comparison. Objective quality measures or measures of comparison are of great importance in the field of image processing. These mea- sures serve as a tool to evaluate and to compare different algorithms designed to solve particular problems, such as noise reduction, de- blurring, compression, ... Consequently these measures serve as a basis on which one algorithm is preferred to another. Furthermore, it is well-known that classical quality measures, such as the RMSE (Root Mean Square Error) or the PSNR(Peak Signal to Noise Ra- tio), do not always correspond to visual observations.

Mixed Noise Removal by Weighted Encoding With Sparse Nonlocal Regularization

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.

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

Abstract: 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.