DeBo: Contrast enhancement for image registration using binary differential evolution and bat optimization

Image registration has demonstrated its significance as an essential tool for target recognition, classification, tracking, and damage assessment during natural catastrophes. The image registration process relies on the identification of numerous reliable features; thus, low resolutions, poor lighting conditions, and low image contrast substantially diminish the number of dependable features available for registration. Contrast stretching enhances image quality, facilitating the object detection process. In this study, we proposed a hybrid binary differential evolution and BAT optimization model to enhance contrast stretching by optimizing a decision variables in the transformation function. To validate its efficiency, the proposed approach is utilized as a preprocessor before feature extraction in image registration. Cross-comparison of detected features of enhanced images verses the original images during image registration validate the improvements in the image registration process.