Image Equalization Using Singular Value Decomposition and Discrete Wavelet Transform
Cagrı Ozcınar, Hasan Demirel, Gholamreza Anbarjafari · InTech eBooks · 2011
Contrast enhancement is frequently referred as one of the most important issues in image processing. Contrast is created by the difference in luminance reflected from two adjacent surfaces. In other words, contrast is the difference in visual properties that makes an object distinguishable from other objects and the background. In visual perception, contrast is determined by the difference in the color and brightness of the object with other objects. Our visual system is more sensitive to contrast than absolute luminance; therefore, we can perceive the world similarly regardless of the considerable changes in illumination conditions. If the contrast of an image is highly concentrated on a specific range, e.g. an image is very dark; the information may be lost in those areas which are excessively and uniformly concentrated. The problem is to optimize the contrast of an image in order to represent all the information in the input image. There have been several techniques to overcome this issue (Shadeed et al., 2003; Gonzales and Woods, 2007; Kim et al., 1998; Chitwong et al., 2002). One of the most frequently used techniques is general histogram equalization (GHE). After the introduction of GHE, researchers came out with better techniques such as local histogram equalization (LHE). However, the contrast issue is yet to be improved and even these days many researchers are proposing new techniques for image equalization. In this work, we are comparing our results with two state-of-art techniques, namely, dynamic histogram equalization (DHE) (Abdullah Al Wadud et al., 2007) and our previously introduced singular value equalization (SVE) (Demirel et al. ISCIS 2008). As motioned before, in many image-processing applications, GHE technique is one of the simplest and most effective primitives for contrast enhancement (Kim and Yang, 2006),