LAUSR

The image haze removal algorithm is challenging regarding computational processing speed and the hazy removal effect. Instead of using the local patch approach, which assumes the scene transmission to be locally constant and uses various filters to smooth the transmission map, this paper proposes a fast single image haze removal method based on a minimum channel and patchless approach. A new simple approach to estimate the atmospheric light and the scene transmission is proposed based on the minimum channel of images. The histogram of the minimum channel of the image is used to extract the atmospheric light pixels and exclude the non-hazy bright pixels in the image. The histogram equalization and image multiplication are applied to achieve better visual quality. In order to verify the performance of the proposed method, 100 images are collected from datasets I-HAZE, O-HAZE, and websites. Experimental results show that our proposed method outperforms up-to-date state-of-the-art haze removal algorithms using quantitative evaluations. From subjective comparisons, the proposed method outperforms most current haze removal algorithms in color restoration. Also, time assessment results show that our proposed method is the fastest among the up-to-date state-of-the-art haze removal methods and is about 15 times faster than the second-fastest method. The main contribution of the proposed method is significantly reducing computation time because it uses a patchless approach that does not need any filter and complicated algorithms. In addition to significantly reducing the computational processing speed, our proposed method can achieve better visual quality.