Aiming at the problems of low contrast and low definition of fog degraded image, this paper proposes an image defogging\nalgorithm based on sparse representation. Firstly, the algorithm transforms image from RGB space to HSI space and uses two-level\nwavelet transform extract features of image brightness components. Then, it uses the K-SVD algorithm training dictionary and\nlearns the sparse features of the fog-free image to reconstructed I-components of the fog image. Using the nonlinear stretching\napproach for saturation component improves the brightness of the image. Finally, convert from HSI space to RGB color space to\nget the defog image. Experimental results show that the algorithm can effectively improve the contrast and visual effect of the\nimage. Compared with several common defog algorithms, the percentage of image saturation pixels is better than the\ncomparison algorithm.
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