Abstract: Because lossy JPEG compression seriously affects the image quality, a jointly image filters based on convolutional neural network is proposed to reduce jpeg compression artifacts. It extracted the feature information by a reversible down-sampling layer to construct the guided feature image. The features from the input image and guided feature image were fused, which was to optionally transfer the common structure to the target image. The filtering output was obtained by skip connection. Based on residual learning, the network jointly trained samples of various compression levels to improve the generalization ability. Compared with theseveral classical algorithms, the proposed algorithm not only effectively removes the JPEG compression artifacts at various compression levels and extracts clearer edge structures, but also has higher peak signal to noise ratio and structural similarity, and the image quality is improved significantly.