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Pyramid-Structured Depth MAP Super-Resolution Based on Deep Dense-Residual Network., , , and . IEEE Signal Process. Lett., 26 (12): 1723-1727 (2019)Perceptual Quality Assessment for Screen Content Images by Spatial Continuity., , , , and . IEEE Trans. Circuits Syst. Video Technol., 30 (11): 4050-4063 (2020)A Novel Objective Quality Assessment Method for Transcoded Videos From H.264/AVC to H.265/HEVC Utilizing Probability Theory., , , , and . TBC, 65 (4): 777-781 (2019)Multi-Stream Dense View Reconstruction Network for Light Field Image Compression., , , , and . IEEE Trans. Multim., (2023)MIG-Net: Multi-Scale Network Alternatively Guided by Intensity and Gradient Features for Depth Map Super-Resolution., , , , , and . IEEE Trans. Multim., (2022)CFNet: Conditional Filter Learning with Dynamic Noise Estimation for Real Image Denoising., , , , and . CoRR, (2022)Multi-Scale Frequency Reconstruction for Guided Depth Map Super-Resolution via Deep Residual Network., , , , , and . IEEE Trans. Circuits Syst. Video Techn., 30 (2): 297-306 (2020)Geometry-assisted multi-representation view reconstruction network for Light Field image angular super-resolution., , , , , and . Knowl. Based Syst., (2023)Visual attention prediction for Autism Spectrum Disorder with hierarchical semantic fusion., , , , , and . Signal Process. Image Commun., (2021)Evaluating the Robustness of Depth Image Super-Resolution Models., , , and . MMSP, page 1-5. IEEE, (2022)