IMPROVEMENT AND ASSESSMENT FOR PC PICTURE DEPENDENT ON HUMAN VISUAL FRAMEWORK

Information Technology

Contrast enhancement plays a significant role in digital image processing. However, traditional histogram equalization usually results in excessive enhancement, which in return causes an unnatural look and loss of details to the target image. In this paper, we propose a novel histogram modification scheme for image contrast enhancement. First, sum of the input histogram and its standard deviation are computed. Then, gamma correction is applied on the result sum to generate a modified histogram. And finally, the traditional histogram equalization is applied on the modified histogram to produce the mapping function. In addition to preserving the mean brightness, the proposed method can enhance an image uniformly with low computation complexity. Extensive experimental results show that the proposed method not only retains features of the input image but also can enhance the contrast of all kind of images significantly. A novel histogram modification scheme for image contrast enhancement is proposed. Outperforms the state-of-art methods on many important image quality assessment metrics. The new scheme is simple to implement with low computational complexity. The algorithm can enhance the contrast significantly and retain image features simultaneously. A more accurate measurement for contrast enhancement is also proposed.

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