Obtaining accurate and reliable data for evaluating the quality of real-world image augmentation is sometimes challenging, which hinders the application of distance measurements for objective assessment. Consequently, subjective evaluation of quality is commonly employed as the most direct and dependable approach to evaluate picture improvement. Dehazing refers to the elimination of atmospheric haze from images depicting settings with reduced visibility caused by haze. The utilization of dark channel priors for single-image dehazing is a widely recognized technique in the area. Nevertheless, the effectiveness of these strategies is restricted to prior knowledge or limitations, rendering them ineffective when photos include a sky area. Image dehazing is a popular method for dealing with the destruction of natural environment shots brought by seasonal changes with reduced contrast, dust, and other disturbances. Images taken in hazy weather have poor contrast and resolution because the haze particles attenuate the light reflected by the target and add unwanted scattering light. The dehazing procedure can triple visual contrast depending on the wave length. The need for low-complexity, high-performance dehazing solutions has risen as a result of developments in autonomous systems and platforms based on contrast-based exposure fusion.