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.

To solve the issues of time-consuming and low recognition rate in the traditional component analysis of rock slices, a method of component analysis of rock slices based on Collaborative Representation (CR) was proposed. Firstly, texture feature of grain in rock slices was discussed, and the way of combining Hierarchical Multi-scale Local Binary Pattern (HMLBP) and Gray Level Co-occurrence Matrix (GLCM) was proved to characterize the texture of grain in rock slices well. Then, in order to reduce the time complexity of classification, the dimension of new features was reduced to 100 by using Principal Component Analysis (PCA). Finally, the Collaborative Representation based Classification (CRC) was used as the classifier. Differ to Sparse Representation based Classification (SRC), prediction samples were encoded by all the samples in train dictionary collaboratively instead of some single sample alone. Same attributes of different samples can improve the recognition rate. The experimental results show that the recognition speed of the method increases by 300% and the recognition rate of the method increases by 2% compared to SRC. In practical application, it can distinguish quartz and feldspar components in rock slices well.

In order to remove the moving cast shadow in the surveillance video quickly and efficiently, an adaptive shadow elimination method based on superpixel and local color constancy of shaded area was proposed. First, the improved simple linear iterative clustering algorithm was used to divide the moving area in the video image into non-overlapping superpixels. Then, the luminance ratio of background and the moving foreground in the RGB color space was calculated, and the local color constancy of shaded area was analyzed. Finally, the standard deviation of the luminance ratio was computed by taking superpixel as basic processing unit, and an adaptive threshold algorithm based on turning point according to the characteristic and distribution of the standard deviation of the shadowed region was proposed to detect and remove the shadow. Experimental results show that the proposed method can process shadows in different scenarios, the shadow detection rate and discrimination rate are both more than 85%; meanwhile, the computational cost is greatly reduced by using the superpixel, and the average processing time per frame is 20 ms. The proposed algorithm can satisfy the shadow removal requirements of higher precision, real-time and robustness.