Image dehazing algorithm based on artificial multi-exposure image fusion

Bad weather conditions such as fog, haze, etc. reduce the visibility level of the images captured in those weather conditions. Degradation of visibility in the captured images is a serious concern nowadays. Even though there are many different devices available for dehazing several devices lack the capability of efficient dehazing, and they are not capable of effectively mitigating the visibility degradation caused by those unusual weather conditions. The image processing task which is concerned with this phenomenon is called image dehazing. In this article, an image fusion-based approach is proposed which eliminates the usage of depth information thus the need for depth calculation is avoided in this method which is a tedious process. This proposed method thus enhances the performance and robustness of the image dehazing process. To achieve this goal the hazy images, undergo a set of gamma corrections operations followed by the fusion of various Gamma-corrected underexposed images obtained from the gamma correction operation followed by color saturation adjustment thus yielding an image with better visibility which is nowadays a major concern as many devices require images with higher quality for processing. Fusion of underexposed images obtained by artificial methods can remove the haze effectively when the situations are challenging. Other image dehazing techniques tend to fail and the proposed method helps to produce better results in terms of PSNR, SSIM, Entropy, accumulation and running time.