2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDTEC-2.1
Paper Title CU-Net+: Deep Fully Interpretable Network for Multi-modal Image Restoration
Authors Jingyi Xu, Xin Deng, Mai Xu, Beihang University, China; Pier Luigi Dragotti, Imperial College London, United Kingdom
SessionTEC-2: Restoration and Enhancement 2
LocationArea G
Session Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Time:Tuesday, 21 September, 15:30 - 17:00
Presentation Poster
Topic Image and Video Processing: Restoration and enhancement
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract The network interpretability is critical in computer vision related tasks, especially for the tasks with multiple modalities. For multi-modal image restoration, one recent method, CU-Net, provides an interpretable network based on a multimodal convolutional sparse coding model. However, its network architecture cannot fully interpret the model. In this paper, we propose to turn the model to networks using recurrent scheme, leading to a fully interpretable network namely CU-Net+. In addition, we relax the constraint on the common and unique feature numbers in CU-Net, for making it more consistent with real condition. The effectiveness of the proposed CU-Net+ is evaluated on RGB guided depth image super-resolution and flash guided non-flash image denoising tasks. The numerical results show that CU-Net+ outperforms other interpretable or non-interpretable methods, with 0.16 RMSE and 0.66 dB PSNR improvement than CU-Net on the two tasks, respectively.