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 secondfastest 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.

Traditional histogram equalization may cause degraded results of over-enhanced images under uneven illuminations. In this paper, a simple and effective image contrast enhancement method is proposed to achieve high dynamic range imaging. First, the illumination of each pixel is estimated by using an induced norm of a patch of the image. Second, a pre-gamma correction is proposed to enhance the contrast of the illumination component appropriately. The parameters of gamma correction are set dynamically based on the local patch of the image. Third, an automatic Contrast-Limited Adaptive Histogram Equalization (CLAHE) whose clip point is automatically set is applied to the processed image for further image contrast enhancement. Fourth, a noise reduction algorithm based on the local patch is developed to reduce image noise and increase image quality. Finally, a post-gamma correction is applied to slightly enhance the dark regions of images and not affect the brighter areas. Experimental results show that the proposed method has its superiority over several state-of-the-art enhancement quality techniques by using qualitative and quantitative evaluations.INDEX TERMS Contrast enhancement, contrast-limited adaptive histogram equalization, gamma correction, high dynamic range, induced norm, noise reduction.

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