Aim: This work aims at developing an artificial intelligence tool which focuses on enhancing the quality of input images. Contrast stretching, also called normalization, is a simple image enhancement technique that attempts to improve the contrast in an image by 'stretching' the range of intensity values it contains to span a desired range of values, the full range of pixel values that the image type concerned allows. In order to enhance the quality of color images an innovative decorrelation stretching algorithm technique is developed in this work. Materials and Methods: In this research, an innovative decorrelation stretching algorithm is proposed and developed for improving the quality of color images and the proposed work is compared with another image enhancement technique called contrast stretching method. Input medical images (N=20) of both groups were downloaded from standard medical databases. The enrollment ratio is obtained as 1 with 95% confidence interval and a threshold value 0.05. Results: The performance of image enhancement is measured using two parameters /namely PSNR and SSIM. These parameters are calculated and evaluated to assess the proposed methods efficacy. High values of PSNR and SSIM indicate better enhancement. High values of PSNR and SSIM indicate better contrast stretching algorithm. Decorrelation stretching algorithm provides the mean PSNR (p=0.006) value of 19.696 and mean SSIM (p=0.004) value of 0.752. Contrast stretching algorithm provides the mean PSNR (p=0.006) value of 21.572 and mean SSIM (p=0.004) value of 0.968. Conclusion: Based on the experiments results from MATLAB software and from independent sample t-test results of IBM-SPSS software, the innovative decorrelation stretching algorithm based image enhancement technique significantly performed better than the contrast stretching algorithm based image enhancement technique with Peak Signal to Noise Ratio and Structural Similarity Index Measure.