A HISTOGRAM MODIFICATION FRAMEWORK AND ITS APPLICATION FOR IMAGE CONTRAST ENHANCEMENT

INTRODUCTION

CONTRAST enhancement plays a crucial role in image processing applications, such as digital photography, medical image analysis, remote sensing, LCD display processing, and scientific visualization. There are several reasons for an image/video to have poor contrast: the poor quality of the used imaging device, lack of expertise of the operator, and the adverse external conditions at the time of acquisition. These effects result in under-utilization of the offered dynamic range. As a result, such images and videos may not reveal all the details in the captured scene, and may have a washed-out and unnatural look. Contrast enhancement targets to eliminate these problems, thereby to obtain a more visually-pleasing or informative image or both. Typical viewers describe the enhanced images as if a curtain of fog has been removed from the picture. Several contrast enhancement techniques have been introduced to improve the contrast of an image. These techniques can be broadly categorized into two groups: direct methods and indirect methods. Direct methods define a contrast measure and try to improve it. Indirect methods, on the other hand, improve the contrast through exploiting the under-utilized regions of the dynamic range without defining a specific contrast term. Most methods in the literature fall into the second group. Indirect methods can further be divided into several subgroups:

 i) techniques that decompose an image into high and low frequency signals for manipulation, e.g., homomorphic filtering,

ii) histogram modification techniques, and

iii) transform-based techniques.

Out of these three subgroups, the second subgroup received the most attention due to its straightforward and intuitive implementation qualities. Contrast enhancement techniques in the second subgroup modify the image through some pixel mapping such that the histogram of the processed image is more spread than that of the original image. Techniques in this subgroup either enhance the contrast globally or locally. If a single mapping derived from the image is used then it is a global method; if the neighbourhood of each pixel is used to obtain a local mapping function then it is a local method. Using a single global mapping cannot (specifically) enhance the local contrast. The method presented in this paper is demonstrated as a global contrast enhancement (GCE) method, and can be extended to local contrast enhancement (LCE) using similar approaches. One of the most popular GCE techniques is histogram equalization (HE). HE is an effective technique to transform a narrow histogram by spreading the gray-level clusters in the histogram, and it is adaptive since it is based on the histogram of a given image. However, HE without any modification can result in an excessively enhanced output image for some applications (e.g., display-processing).