Contrast Limited Adaptive Histogram Equalization based Multi-Objective Improved Cat Swarm Optimization for Image Contrast Enhancement

The contrast enhancement technique is utilized to enhance the image quality which produce good quality images for various image processing tasks. The Histogram Equalization (HE) is one of the widely utilized technique for enhance the contrast and estimates the intensity frequency in an image. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is one of the local HE technique. Therefore, the CLAHE can handle the issues of classical HE algorithm. The CLAHE utilizes two various parameters such as number of tiles and clip limit, moreover the effectiveness depends on these two parameters. In this paper, Multi-Objective Improved Cat Swarm Optimization (MOICSO) is proposed to determine the optimum parameters for CLAHE to improve the image contrast. The first fitness function is utilized for estimating entropy and second is utilized for calculating Fast Noise Variance Estimation (FNVE) which produce local preservations and avoid noise amplification in an output image. The parameters like Mean Squared Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-noise Ratio (PSNR) and Computing Time (CT) are utilized to evaluate the proposed methodology. The obtained result shows that the MOICSO achieves less MAE of 6.28 which is which is better than other existing methods.