In this paper, we propose an image adjustment method for multi-exposure images based on convolutional neural networks (CNNs). We call image regions without information due to saturation and object moving in multi-exposure images lacking areas in this paper. Lacking areas cause the ghosting artifact in fused images from sets of multi-exposure images by conventional fusion methods, which tackle the artifact. To avoid this problem, the proposed method estimates the information of lacking areas via adaptive inpainting. The proposed CNN consists of three networks, warp and refinement, detection, and inpainting networks. The second and third networks detect lacking areas and estimate their pixel values, respectively. In the experiments, it is observed that a simple fusion method with the proposed method outperforms state-of-the-art fusion methods in the peak signal-to-noise ratio. Moreover, the proposed method is applied for various fusion methods as pre-processing, and results show obviously reducing artifacts.

This paper reports on the suitability of the SUSAN filter for the removal of artifacts that result from quantization errors in wavelet video coding. In this paper two extensions of the original filter are described. The first uses a combination of 2-D spatial filtering followed by 1-D temporal filtering along motion trajectories, while the second extension is a pure 3-D motion compensated SUSAN filter. The SUSAN approach effectively reduces coding artifacts, while preserving the original signal structure, by relying on a simple pixel-difference-based classification procedure. Results reported in the paper clearly indicate that both extensions efficiently reduce ringing that is the prevalent artifact perceived in wavelet-based coded video. Experimental results indicate an increase in perceptual as well as objective (PSNR) decoded video quality, which is competitive with state-of-the-art post-processing algorithms, especially when low computational demands of the proposed approach are taken into account.