An infrared image dehazing method based on modified dark channel prior
Siyu Yan, Jingwen Zhu, Kai Yun, Yuexing Wang, Chuangang Xu · 2022
To improve the effectiveness of the infrared image dehazing algorithm, we modify the DCP based on the observation of several outdoor haze-free images: in these images, pixels of most local patches no longer have low intensity but are close to high intensity. The transmission map is estimated through the MDCP, and the guided filter and CLAHE achieve enhancement. The haze infrared images can be recovered completely. Moreover, to thrive the infrared dehazing research, we introduce the IR Dense-haze dataset, which contains 13 pairs of synthetic haze infrared images and corresponding haze-free infrared images and six extra haze infrared images captured in natural scenes. Experiments on the IR Densehaze dataset show that our method achieves dominant performance. The average PSNR of the infrared image is 39.02% higher than the original DCP method, and the average SSIM is 28.65% higher than the original DCP method.