An adaptive bi- l _p- l ₂-norm based blind super-resolution reconstruction method was proposed to improve the quality of a low-resolution blurred image, which includes independent blur-kernel estimation sub-process and non-blind super-resolution reconstruction sub-process. In the blur-kernel estimation sub-process, the bi- l _p- l ₂-norm regularization was imposed on both the sharp image and the blur-kernel. Moreover, by introducing threshold segmentation of image gradients, the l _p-norm and the l ₂-norm constraints on the sharp image were adaptively combined. With the estimated blur-kernel, the non-blind super-resolution reconstruction method based on non-locally centralized sparse representation was used to reconstruct the final high-resolution image. In the simulation experiments, compared with the bi- l ₀- l ₂-norm based method, the average Peak Signal-to-Noise Ratio (PSNR) gain of the proposed method was 0.16 dB higher, the average Structural Similarity Index Measure (SSIM) gain was 0.0045 higher, and the average reduction of Sum of Squared Difference (SSD) ratio was 0.13 lower. The experimental results demonstrate a superior performance of the proposed method in terms of kernel estimation accuracy and reconstructed image quality.

An improved method for Multi-Scale Retinex with Color Restoration (MSRCR) algorithm was proposed, to remove the fog at the far prospect and solve gray hypothesis problem. First, original fog image was inverted. Then, MSRCR algorithm was used on it. The inverted image was to be inverted again and then was linearly superposed with the result which was processed by MSRCR algorithm directly .At the same time , the reflection component which was got during the process of the extraction was linearly superposed with the original luminance, and the mean and variance were calculated to decide the contrast stretching degree adaptively, finally, it was uniformly stretched to the display device.The experimental results show that the proposed algorithm can get a better effect of removing the fog. Evaluation values of the processed image, including standard difference, average brightness, information entropy, and squared gradient, are improved than the original algorithm. It is easy to implement and has important significance for real-time video to remove fog.