Abstract
An effective method to enhance the contrast of digital images is proposed in this paper. A histogram function is developed to make the histogram curve smoother, which can be used to avoid the loss of information in the processed image. Besides the histogram function, an adaptive gamma correction for the histogram is proposed to stretch the brightness contrast. Moreover, the log-exp transformation strategy is presented to progressively increase the low intensity while suppressing the decrement of the high intensity. In order to further widen the dynamic range of the image, the nonlinear normalization transformation is put forward to make the output image more natural and clearer. In the experiment on non-uniform illumination images, the average contrast per pixel (CPP), root mean square (RMS), and discrete entropy (DE) metrics of the developed approach are shown to be superior to selected state-of-the-art methods.
Highlights
IntroductionEnhancement technology is regarded as one of the most fundamental problems in computer vision
Enhancement technology is regarded as one of the most fundamental problems in computer vision.It can be widely used in many applications such as monitoring, imaging systems, human–computer interactions, and so on [1,2,3]
In view of the above problems, many researchers still concentrate on poor contrast image enhancement [4,5]
Summary
Enhancement technology is regarded as one of the most fundamental problems in computer vision. It can be widely used in many applications such as monitoring, imaging systems, human–computer interactions, and so on [1,2,3]. The captured scenes are under unfavorable environmental conditions—for example, the presence of clouds, the lack of sunlight, backlight or indoor lighting, and so on. These conditions might result in reduced contrast quality. The direct enhancement methods [6,7,8]
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