Modified dark channel prior based on multi-scale Retinex for power image defogging

This article has been retracted, and the retraction notice can be found here: http://dx.doi.org/10.4108/eai.8-4-2022.173800. At present, defogging technologies can be roughly divided into two categories: the first category is the method of defogging based on image enhancement non-physical model. This method does not start from the essence of optical imaging, but only improves the visual effect of the image by improving the contrast and color of the image, so as to achieve the purpose of defogging. The commonly used methods include histogram equalization, contrast enhancement and automatic color levels, Retinex theory and wavelet transform, etc. The second type is based on atmospheric scattering physical model. This method analyzes the degradation mechanism in the process of imaging, establishes the degradation model of foggy image, and restores the real scene without fog by using the prior knowledge in the degradation process. This method needs to obtain prior conditions as model parameters, but the prior conditions are often difficult to obtain. In this paper, an adaptive power image defogging algorithm based on multi-scale Retinex and modified dark channel is proposed. Sobel operator is used to detect the edges of luminance components and multi-scale Retinex algorithm is used to eliminate luminance components. A priori theory of dark channel optimization by guided filtering is used to obtain rough estimated transmittance. The global atmospheric light value is selected by quadtree subspace search method. In order to eliminate the phenomenon that the restored image is dark as a whole and cannot display details, the brightness value is corrected by two-dimensional gamma function, and finally the restored defogging image is obtained. The experimental results show that the proposed algorithm can effectively restore the details of foggy images, completely remove foggy images, have good color brightness, and the images are clear and natural.