The DOI matches its Crossref record; 80 of the 80 references it cites were found in a registry or an index.
Deep Convolutional Neural Network for Multi-Modal Image Restoration and Fusion
Open accessAbstract
In this paper, we propose a novel deep convolutional neural network to solve the general multi-modal image restoration (MIR) and multi-modal image fusion (MIF) problems. Different from other methods based on deep learning, our network architecture is designed by drawing inspirations from a new proposed multi-modal convolutional sparse coding (MCSC) model. The key feature of the proposed network is that it can automatically split the common information shared among different modalities, from the unique information that belongs to each single modality, and is therefore denoted with CU-Net, i.e., common and unique information splitting network. Specifically, the CU-Net is composed of three modules, i.e., the unique feature extraction module (UFEM), common feature preservation module (CFPM), and image reconstruction module (IRM). The architecture of each module is derived from the corresponding part in the MCSC model, which consists of several learned convolutional sparse coding (LCSC) blocks. Extensive numerical results verify the effectiveness of our method on a variety of MIR and MIF tasks, including RGB guided depth image super-resolution, flash guided non-flash image denoising, multi-focus and multi-exposure image fusion.
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Deep Residual Learning for Image Recognition
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Image quality assessment: from error visibility to structural similarity
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Perceptual Losses for Real-Time Style Transfer and Super-Resolution
Johnson, J., Alahi, A., & Fei-Fei, L. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46475-6_43 · Cited by 11,283
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Zhang, K., Zuo, W., Chen, Y., Meng, D., & Zhang, L. (2017) · IEEE Transactions on Image Processing · doi:10.1109/tip.2017.2662206 · Cited by 8,983
Accurate Image Super-Resolution Using Very Deep Convolutional Networks
Kim, J., Lee, J. K., & Lee, K. M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.182 · Cited by 7,870
Enhanced Deep Residual Networks for Single Image Super-Resolution
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Image Style Transfer Using Convolutional Neural Networks
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DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Zhao, Z., Bai, H., Zhu, Y., Zhang, J., Xu, S., Zhang, Y., Zhang, K., Meng, D., Timofte, R., & Van Gool, L. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv51070.2023.00742 · Cited by 431
MoCoLSK: Modality-Conditioned High-Resolution Downscaling for Land Surface Temperature
Dai, Q., Yuan, C., Dai, Y., Li, Y., Li, X., Ni, K., Xu, J., Shu, X., & Yang, J. (2025) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2025.3547945 · Cited by 5
Metadata
- Authors
- Deng, X., & Dragotti, P. L.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2021-10-01
- Volume
- 43
- Issue
- 10
- Pages
- 3333-3348
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- 1939-3539
- OpenAlex
- W3014859219
- Cited by
- 282
- References
- 80
- FWCI
- 10.51
- Type
- journal article
- Language
- en
- Open access
- green
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