Fast image haze-removal algorithm based on mixed filter

XinYu He; Chengjun Xie

doi:10.1117/12.2284382

24 October 2017 Fast image haze-removal algorithm based on mixed filter

XinYu He, Chengjun Xie

Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 1046226 (2017) https://doi.org/10.1117/12.2284382
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China

Abstract

According to the theory of dark channel prior a image haze-removal algorithm is proposed in this paper. The algorithm uses maximum-minimum value filter combined together with guided filter to remove haze from the original image and uses wavelet to enhance the visual effect of the de-hazed image. Using maximum-minimum value filter only can cause the problem that the algorithm depending on the value of transmission lower limit excessively, by using maximum-minimum value filter combined together with guided filter the problem can be solved efficiently and the transmission matrix is refined adaptively. The white halos and patchy singularities which exist at the edge of the depth field in the reconstructed image is eliminated. Furthermore the algorithm refine the values of transmission which are estimated too big or too small. Finally wavelet is adopted to enhance the visual effect of the de-hazed image effectively. The objective evaluations of the reconstructed de-hazed image such as reconstructed image entropy, reconstructed image variance, reconstructed image mean square error, the degree of reconstructed image change and reconstructed image clarity are also studied in the paper, but these indicators can not represent the advantages and disadvantages of the performance of the image haze-removal algorithm, so it still needs further study in this field.

Citation Download Citation

XinYu He and Chengjun Xie "Fast image haze-removal algorithm based on mixed filter", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 1046226 (24 October 2017); https://doi.org/10.1117/12.2284382

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