Contrast and brightness balance in image enhancement using Cuckoo Search-optimized image fusion

Many vision-based systems suffer from poor levels of contrast and brightness, mainly because of inadequate and improper illumination during the image acquisition process. As a result, the required specified information from the acquired image is not available for the particular application. In general, it is hard to achieve a balance between the improvement of contrast and brightness in image enhancement. By introducing nature-inspired optimization in image enhancement, the best features of the image are utilized, and the complexity related to the nonlinearity of images can be solved with various constraints, like a balance between contrast and brightness. In this work, a novel automatic method for image enhancement to find a balance between contrast and brightness is developed by using Cuckoo Search-optimized image fusion. First, the Cuckoo Search-based optimization algorithm generates two sets of optimized parameters. These parameter sets are used to generate a pair of enhanced images, one with a high degree of sharpness and contrast, the other is bright and has been improved without losing the level of detail. Furthermore, the two enhanced images are fused by the fusion process to obtain an output image where the contrast and brightness are in balance. The effectiveness of the proposed method is verified by applying it to standard images (CVG-UGR image database) and lathe tool images. Experimental results demonstrated that the proposed method performs better with regard to both the quality of contrast and brightness, moreover, yields enhanced quality evaluation metrics compared to the other conventional techniques. (C) 2021 The Authors. Published by Elsevier B.V. on behalf of King Saud University.