"基于补丁低秩正则化的图像恢复技术及实验比较分析"

Image restoration refers to the process of recovering the original appearance of a degraded or damaged image. One popular method for image restoration is patch-based low-rank regularization, which utilizes patches of the image to restore the overall image quality. This technique involves inpainting, which is the process of filling in missing or damaged parts of an image. Fang Li from East China Normal University, along with X. Lv, have collaborated on a joint work that focuses on image restoration using patch-based low-rank regularization. Their proposed method utilizes a variational formulation of Patch-wise Non-local Nuclear Norm Minimization (PWNNM), which helps to preserve image details while reducing noise and artifacts. The proposed model and algorithm make use of a combination of patch-based techniques and low-rank regularization to restore images with high fidelity. Through convergence analysis, the researchers have shown the effectiveness and robustness of their approach in restoring images that have been degraded by various factors such as blurring or noise. In their experiments and comparisons, Fang Li and X. Lv demonstrate the effectiveness of their method in image inpainting and deblurring. By applying their patch-based low rank regularization technique, they are able to achieve superior results compared to other traditional methods. This research showcases the potential of using patch-based low-rank regularization for image restoration and inpainting tasks. Overall, Fang Li and X. Lv's work highlights the importance of utilizing patch-based techniques and low-rank regularization for image restoration. Their method shows promising results in restoring degraded images and inpainting missing regions, making it a valuable contribution to the field of image processing.