D. Van der Weken

Noise reduction by fuzzy image filtering

A new fuzzy filter is presented for the noise reduction of images corrupted with additive noise. The filter consists of two stages. The first stage computes a fuzzy derivative for eight different directions. The second stage uses these fuzzy derivatives to perform fuzzy smoothing by weighting the contributions of neighboring pixel values. Both stages are based on fuzzy rules which make use of membership functions. The filter can be applied iteratively to effectively reduce heavy noise. In particular, the shape of the membership functions is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. A statistical model for the noise distribution can be incorporated to relate the homogeneity to the adaptation scheme of the membership functions. Experimental results are obtained to show the feasibility of the proposed approach. These results are also compared to other filters by numerical measures and visual inspection.

A fuzzy impulse noise detection and reduction method

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 Two-Step Filter for Impulse Noise Reduction From Color Images

A new framework for reducing impulse noise from digital color images is presented, in which a fuzzy detection phase is followed by an iterative fuzzy filtering technique. We call this filter the fuzzy two-step color filter. The fuzzy detection method is mainly based on the calculation of fuzzy gradient values and on fuzzy reasoning. This phase determines three separate membership functions that are passed to the filtering step. These membership functions will be used as a representation of the fuzzy set impulse noise (one function for each color component). Our proposed new fuzzy method is especially developed for reducing impulse noise from color images while preserving details and texture. Experiments show that the proposed filter can be used for efficient removal of impulse noise from color images without distorting the useful information in the image

Fuzzy Filters for Image Processing

1. Fuzzy Filters for Noise Removal.- 2. Fuzzy Filters for Noise Reduction in Images.- 3. Real-time Image Noise Cancellation Based on Fuzzy Similarity.- 4. Fuzzy Rule-Based Color Filtering Using Statistical Indices.- 5. Fuzzy Based Image Segmentation.- 6. Fuzzy Thresholding and Histogram Analysis.- 7. Color Image Segmentation by Analysis of 3D Histogram with Fuzzy Morphological Filters.- 8. Fast and Robust Fuzzy Edge Detection.- 9. Fuzzy Data Fusion for Multiple Cue Image and Video Segmentation.- 10. Fuzzy Image Enhancement in the Framework of Logarithmic Models.- 11. Observer-Dependent Image Enhancement.- 12. Fuzzy Techniques in Digital Image Processing and Shape Analysis.- 13. Adaptive Fuzzy Filters and Their Application to Online Maneuvering Target Tracking.- 14. Lossy Image Compression and Reconstruction Based on Fuzzy Relational Equation.- 15. Avoidance of Highlights through ILFOs in Automated Visual Inspection.- Appendix Color Images.

Fuzzy Filters for Noise Reduction: The Case of Gaussian Noise

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

New fuzzy filter for Gaussian noise reduction

A new fuzzy filter is presented for the noise reduction of images corrupted with additive Gaussian noise. The filter consists of two stages. The first stage computes a fuzzy gradient for eight different directions around the currently processed pixel. The second stage uses the fuzzy gradient to perform fuzzy smoothing by taking different contributions of neighboring pixel values. The two stages are both based on fuzzy rules which make use of membership functions. The filter can be applied iteratively to effectively reduce heavy noise. The shape of the membership functions is adapted according to the remaining noise level after each iteration, making use of the distribution of the homogeneity in the image. The fuzzy operators are implemented by the classical min/max. Experimental results are obtained to show the feasibility of the proposed approach. These results are also compared to other filters by numerical measures and visual inspection.

An overview of classical and fuzzy-classical filters for noise reduction

In this paper we give an overview of classical and fuzzy-classical filters for image noise reduction. Together with our overview (2001) of fuzzy filters, this paper can be seen as a preparation to our comparative study of classical and fuzzy filters for image noise reduction.

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

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.

A comparative study of classical and fuzzy filters for noise reduction

In this paper we present the results of a comparative study of classical and fuzzy filters for image noise reduction. The discussed fuzzy filters are classified, and their performance is compared with classical filters and evaluated by numerical and visual experiments.

A New Fuzzy Filter for the Reduction of Randomly Valued Impulse Noise

In many current impulse noise models for images, corrupted pixels are replaced with values equal or near the maximum or minimum intensity values of the allowable dynamic range. In this paper, we present a new fuzzy filter for a more general noise model in which a noisy pixel has an arbitrary value in the dynamic range according to some underlying probability distribution. This filter consists of (1) a fuzzy detection method, where we investigate if a certain pixel position can be seen as noisy or not and (2) a fuzzy reduction method that reduces the noise while preserving the fine details (like edges and textures) of the image. Experimental results have shown that the proposed filter may be used for efficient removal of randomly valued impulse noise without distorting the useful information in the image.