Foggy images taken in the bad weather inevitably suffer from contrast loss and color distortion. Existing defogging methods merely resort to digging out an accurate scene transmission in ignorance of their unpleasing distortion and high complexity. Different from previous works, we propose a simple but powerful method based on histogram equalization and the physical degradation model. By revising two constraints in a variational histogram equalization framework, the intensity component of a fog-free image can be estimated in HSI color space, since the airlight is inferred through a color attenuation prior in advance. To cut down the time consumption, a general variation filter is proposed to obtain a numerical solution from the revised framework. After getting the estimated intensity component, it is easy to infer the saturation component from the physical degradation model in saturation channel. Accordingly, the fog-free image can be restored with the estimated intensity and saturation components. In the end, the proposed method is tested on several foggy images and assessed by two no-reference indexes. Experimental results reveal that our method is relatively superior to three groups of relevant and state-of-the-art defogging methods.

No abstract

In order to improve the quality of haze degraded image, a novel method is proposed combining dark channel prior and the atmospheric degradation model. Firstly, adaptive block is performed to acquire the dark channel image; then, in HSV space, laplacian pyramid filter is required to obtain the low pass portion of V, further compensated by the single scale Retinex to achieve the final atmospheric light; moreover, for the smoothness trait of guided filter and its fast operation rate, it is suggested to gain the optimal transmission map. Experimental results show that this algorithm can effectively improve the visibility of the optical imaging system.

No abstract

No abstract