A Fuzzy Homomorphic Algorithm for Image Enhancement

Abstract

The implementation and analysis of a novel Fuzzy Homomorphic image enhancement technique is presented. The he implementation and analysis of a novel Fuzzy Homomorphic image enhancement technique is presented. The he implementation and analysis of a novel Fuzzy Homomorphic image enhancement technique is presented. The he implementation and analysis of a novel Fuzzy Homomorphic image enhancement technique is presented. The technique technique technique technique combines the logarithmic transform with fuzzy membership functions to deliver an intuitive method of combines the logarithmic transform with fuzzy membership functions to deliver an intuitive method of combines the logarithmic transform with fuzzy membership functions to deliver an intuitive method of combines the logarithmic transform with fuzzy membership functions to deliver an intuitive method of image enhancement. This algorithm reduces the computational complexity by eliminating the need for image image enhancement. This algorithm reduces the computational complexity by eliminating the need for image image enhancement. This algorithm reduces the computational complexity by eliminating the need for image image enhancement. This algorithm reduces the computational complexity by eliminating the need for image dependent filter kernels and the forward and dependent filter kernels and the forward and dependent filter kernels and the forward and dependent filter kernels and the forward and with the more established algorithms with the more established algorithms with the more established algorithms with the more established algorithms for the enhancement of low contrast images with uneven illumination for the enhancement of low contrast images with uneven illumination for the enhancement of low contrast images with uneven illumination for the enhancement of low contrast images with uneven illumination results show that the fuzzy method provides similar or better results than results show that the fuzzy method provides similar or better results than results show that the fuzzy method provides similar or better results than results show that the fuzzy method provides similar or better results than other well other well other well other well-known image enhancement algorithms. known image enhancement algorithms. known image enhancement algorithms. known image enhancement algorithms.

Automated crack segmentation via saturation channel thresholding, area classification and fusion of modified level set segmentation with Canny edge detection

“…We first convert the image to grayscale if it is an RGB colour image to reduce data and processing time. Then we perform illumination correction using a previously developed fuzzy homomorphic enhancement algorithm [ 8 , 55 , 56 ]. This allows the normalization of illumination without any edge or noise enhancement.…”

Partial Differential Equation‐Based Enhancement and Crack Detection

“…This can be mitigated using tonal mapping operators such as Homomorphic filter (HF) and Retinex. We use a previously devised fuzzy Homomorphic enhancement (FHE) algorithm [13] to normalize the image intensities where necessary. For the problem of similarity of crack foreground and background intensities, we first analyze a row profile of the input image, which is the usual practice in the field [12].…”