Fast and memory efficient de-hazing technique for real-time computer vision applications

Some features of an image may spoil due to fog or haze, smoke. These images lose their brightness due to air-light scattering. It offers troublesomeness to the people lives in hill and fog regions of the world. This paper proposed two key aspects. One is a modified dark-channel method based on the median for eliminating the refine the transmission map as well as halos and artifacts, another important aspect is a memory-efficient row-based arrangement of the pixels for real-time applications. The advantage of this method is air-light can be predicted directly from the modified dark channel and also accurate transmission map can be estimated. This method is compared with other existing four algorithms. Our proposed method analyzed in terms of Peak Signal to Noise Ratio (PSNR), Average Time cost (ATC), percentage of haze improvement (PHI), average contrast of output image (ACOI), Mean Squared Error (MSE) and Structural Similarity Index (SSIM). The quality of the output de-haze image of our algorithm over existing algorithms is more. It has taken less computation time, equal MSE, with higher SSIM and has more percentage of haze improved over existing methods.