Adaptive image contrast enhancement based on human visual properties

Abstract

Existing methods for image contrast enhancement focus mainly on the properties of the image to be processed while excluding any consideration of the observer characteristics. In several applications, particularly in the medical imaging area, effective contrast enhancement for diagnostic purposes can be achieved by including certain basic human visual properties. Here the authors present a novel adaptive algorithm that tailors the required amount of contrast enhancement based on the local contrast of the image and the observer's Just-Noticeable-Difference (JND). This algorithm always produces adequate contrast in the output image, and results in almost no ringing artifacts even around sharp transition regions, which is often seen in images processed by conventional contrast enhancement techniques. By separating smooth and detail areas of an image and considering the dependence of noise visibility on the spatial activity of the image, the algorithm treats them differently and thus avoids excessive enhancement of noise, which is another common problem for many existing contrast enhancement techniques. The present JND-Guided Adaptive Contrast Enhancement (JGACE) technique is very general and can be applied to a variety of images. In particular, it offers considerable benefits in digital radiography applications where the objective is to increase the diagnostic utility of images. A detailed performance evaluation together with a comparison with the existing techniques is given to demonstrate the strong features of JGACE.

Global-Local Contrast Enhancement

“…Although GHE is one of the most frequently used enhancement methods, it is not always desirable [19], because either over enhancement in some parts of the output image or under exposure in some another parts of the same output image is occurred. In another sense, some information may be lost in the output image.…”

Local Contrast Enhancement using Local Standard Deviation

“…And also, the choosing of the window size is dependent on the particular image so that how much equal gray values of contiguous pixels are present in the image. On the other hand, we can choose any desired size of the window for the second method i.e., equation (6). Hence, the second technique is the better method.…”

“…Global Histogram Equalization (GHE) is very effective and the most appropriate enhancement method for some images. However, GHE is not always desirable [6], because it produces the image over enhanced in some portions and some properties of the image cannot be defined properly.…”

Image Contrast Enhancement by Global and Local Adjustment of Gray Levels

“…Contrast enhancement attempts to increase global contrast and local details of the degraded image. Numerous methods have been proposed to improve the contrast of images [1]- [9]. Most contrast enhancement methods can be classified into two main categories: global contrast enhancement (GCE) methods and local contrast enhancement (LCE) methods.…”

“…In some applications, the local details are more important than global contrast. Adaptive contrast enhancement (ACE) method using unsharp masking techniques is a well-known LCE method [8], [9]. The main issue in the ACE method is how to determine a contrast gain (CG) that results in the ringing effect and noise over-enhancement [8].…”