Multi-parameter modification based color image visuality enhancement

Abstract

Image visuality enhancement is an important part in the field of image processing for betterment of visual and informational quality of a distorted image. Various parameters are there for improving such quality like contrast, sharpness, intensity etc. Histogram Equalization (HE) technique is very popular approaches for enhancing contrast and preserving its main characteristics. But conventional HE techniques are not so suitable for maintaining all the image characteristics to enrich the overall image quality. In this regard, optimization techniques provide better result by selecting proper parameters. But histogram equalization based optimization techniques can only improve the image contrast, whether it is not capable for improving the sharpness as well as the intensity of the distorted images for overall betterment of the image quality. This paper shows the implementation of various renowned methods such as Homomorphic Filtering, Discrete Wavelet Transform (DWT), Unsharp Masking (USM) to improve the intensity and sharpness of the input images and finally the effective output of these methods has been implemented with the search dynamics of Artificial Bee Colony (ABC) techniques to get better contrast enhancement while optimizing the objective function designed towards preserving the important characteristics of the distorted images. This method is verified with different test images. The output images are compared with the corresponding input images in both visually as well as against different image quality metrics. The visual results and the metric based outputs proved the potential of the presented method over the existing techniques.