There are numerous video processing algorithms and modules available. When the algorithms are not optimally tuned, undesired results may happen in the processed video signals, e.g. blurring, overshoots/downshoots, loss of details and aliasing. When the video processing modules are fixed, e.g. when the modules are implemented in hardware/chips, it is highly desirable to repair those unpleasant effects caused by certain imperfect algorithms. In this paper, we propose a solution based on classification and least squares trained filters to repair/patch low-quality video processing modules at the back end of a video chain. Extensive experiments show that the repairing method can significantly improve the video quality without modifying the original processing modules.