In view of the problems of poor clarity, low contrast, and dark color of traditional image defogging algorithms, an improved defogging algorithm was proposed for processing images with sea fog. First, the dark channel image and the minimum image of foggy image were obtained. The foggy image was converted into the HSV color space to calculate the color decay rates of each pixel, which were then sorted in descending order, and the minimum value of the first 10% values was taken as the threshold of the bright and dark parts. On this basis, the dark part (I_{HSV_dark}) of the foggy image was calculated. The variational function was introduced to determine whether the pixel in the image was from the bright area, and the variational dark part (I_{VAM_dark}) based on the variational function was obtained. Then, the two dark parts were combined to calculate the dark part image (I_dark), which was used to estimate the ambient light value of the dark area. The pixel values were sorted in descending order, and the average value of the degraded image pixels corresponding to the top 1‰ pixel values was selected as the value of A_dark. Next, a method for removing textures was proposed based on the multi-level weight relative total variation model. The minimum image was filtered as the rough estimate of the transmittance image, which was adjusted by the transmittance function to weaken the defogging of the bright image and enhance the defogging of the dark image. Finally, a minimum variance median guide filter algorithm was proposed to optimize the adjusted transmittance, and the clear image was acquired based on the foggy image degradation model. Experimental results show that the information entropy, average gradient, contrast, and fog aware density evaluator (FADE) of the restored image obtained by the proposed algorithm were significantly improved compared with traditional algorithms.