Interactive image enhancement by fuzzy relaxation

Abstract

In this paper, an interactive image enhancement (IIE) technique based on fuzzy relaxation is presented, which allows the user to select different intensity levels for enhancement and intermit the enhancement process according to his/her preference in applications. First, based on an analysis of the convergence of a fuzzy relaxation algorithm for image contrast enhancement, an improved version of this algorithm, which is called FuzzIIE Method 1, is suggested by deriving a relationship between the convergence regions and the parameters in the transformations defined in the algorithm. Then a method called FuzzIIE Method 2 is introduced by using a different fuzzy relaxation function, in which there is no need to re-select the parameter values for interactive image enhancement. Experimental results are presented demonstrating the enhancement capabilities of the proposed methods under different conditions.

An effective local contrast enhancement technique by blending of local statistics and Genetic Algorithm

“…The is a range that splits the interval ( , ) and it can be determined as (13) Step 2: Determine the fitness of each chromosome as follows (14) In Eq. (14), is the PSNR value of the enhanced image, where the local contrast enhance ment is accomplished using -mentioned C f .…”

“…Image enhancement processes utilize a collection of techniques and attempt to enhance the visual appear ance of an image or convert it for better analysis by a human or machine [3,9]. The built in information in the original image is not increased by image enhance ment, but it is useful for further image applications, for example aiding image segmentation, or for identifying and interpreting beneficial information in the image by implementing the capability of human or machine recognition systems [14]. For modifying image bright ness, contrast or gray level distributions, enhancement operations are utilized.…”

Statistic learning-based defect detection for twill fabrics

“…However, there exists inhomogeneity in illumination in the image, which needs image enhancement before detection. Image contrast enhancement techniques selectively emphasize and inhibit certain information in an image to strengthen the usability of the image [14] . To improve the detection performance, the illumination of an image is processed in advance.…”

Virtual reality: A state-of-the-art survey

“…These methods can remove noise but they obscure the edge as well. Further, the improvement in traditional algorithms, fuzzy mathematics [15] , wavelet transformation [16] , and partial differential equations (PDE) [17] have been widely adopted in image noise removal and restoration. Furthermore, genetic algorithm [18] , neural net algorithm [19] , mathematical morphologic theory [20] , and rough sets [21] are also introduced into image noise removal and restoration.…”