FIRE operators for image processing

Enhancement of noisy image data is a very challenging issue in many research and application areas. In the last few years, nonlinear filters based on fuzzy models have been shown to be very effective in removing noise without destroying the useful information contained in the image data. The aim of this paper is to provide a comprehensive overview of most significant methods in the literature and to present some new advances in the field of evolutionary neural fuzzy filters.

Recent advances in fuzzy techniques for image enhancement

This paper presents a new fuzzy-logic-control based filter with the ability to remove impulsive noise and smooth Gaussian noise, while, simultaneously, preserving edges and image details efficiently. To achieve these three image enhancement goals, we first develop filters that have excellent edge-preserving capability but do not perform well in smoothing Gaussian noise. Next, we modify the filters so that they perform all three image enhancement tasks. These filters are based on the idea that individual pixels should not be uniformly fired by each of the fuzzy rules. To demonstrate the capability of our filtering approach, it was tested on several different image enhancement problems. These experimental results demonstrate the speed, filtering quality, and image sharpening ability of the new filter.

A new fuzzy logic filter for image enhancement

This chapter is devoted to introduce a new filtering approach based on fuzzy-logic control concepts with the properties of removing impulsive noise and smoothing out Gaussian noise while, simultaneously, preserving edges and image details efficiently The main idea behind the proposed filtering approach is that each pixel is not allowed to be uniformly fired by each of the fuzzy rules In this chapter, different modifications of this filtering approach (Iterative Fuzzy Control based Filter — IFCF) named by MIFCF, EIFCF, SFCF, SSFCF, FFCF, AFCF and ACFCF are presented along with some test experiments highlighting the merit of each filter From the experimental results we may list the concluding remarks of the proposed filtering approach: high quality of edge preserving ability, high filtering quality especially for complex images, multiplicative noise removing property for IFCF based filters, floating point free calculations and very fast performance for FFCF based filters

A Fuzzy Logic Control Based Approach for Image Filtering

The most significant feature of diagnostic medical images is to reduce Gaussian noise which is commonly found in medical images and make better image quality. In recent years, technological development has significantly improved analyzing medical imaging. This paper proposes different hybrid filtering techniques for the removal of Gaussian noise, by topological approach. The filters are treated in terms of a finite set of certain estimation and neighborhood building operations. A set of such operations is suggested on the base of the analysis of a wide variety of nonlinear filters described in the literature. The quality of the enhanced images is measured by the statistical quantity measures: Root Mean Square Error (RMSE) and Peak Signal-to-Noise Ratio (PSNR).

New hybrid filtering techniques for removal of gaussian noise from medical images

We present a new hybrid filter design methodology. Our hybrid filter consists of a nonlinear impulsive noise filter and a fuzzy weighted linear filter. This hybrid filter can efficiently remove large amounts of mixed Gaussian and impulsive noise while preserving the image details. The performance and robust stability of the proposed filter are compared to linear filters and nonlinear filters theoretically and experimentally.

S. Peng

Papers

A hybrid filter for image enhancement