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3 October 2024 Single image dehazing based on multiscale feature fusion and interaction
Sheng Li, Shengjie Yang, Kun Meng, Haibin Liao, Li Yuan
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Proceedings Volume 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024); 132720T (2024) https://doi.org/10.1117/12.3048398
Event: 5th International Conference on Computer Vision and Data Mining (ICCVDM 2024), 2024, Changchun, China
Abstract
To address issues such as incomplete detail restoration and partial loss of structural information during the dehazing process, a single image dehazing algorithm based on multi-scale feature fusion and interaction was proposed. The algorithm based on U-Net network architecture and utilizes heterogeneous convolutions to extract and fuse multi-scale features, thereby effectively reducing the loss of image detail features during down-sampling. Simultaneously, during image reconstruction, the paper proposes an image enhancement module constructed based on feature fusion and interaction, which improve the reconstruction accuracy and generalization of the model. Experimental results show that the proposed network achieves an SSIM of 0.9896 and a PSNR of 35.89dB on the SOTS-outdoor dataset. Compared to dehazing algorithms such as MSBDN, De-Hamer, and gUNet-s, the proposed network's SSIM and PSNR have average improvements of 0.04 and 6.02dB, respectively.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Sheng Li, Shengjie Yang, Kun Meng, Haibin Liao, and Li Yuan "Single image dehazing based on multiscale feature fusion and interaction", Proc. SPIE 13272, Fifth International Conference on Computer Vision and Data Mining (ICCVDM 2024), 132720T (3 October 2024); https://doi.org/10.1117/12.3048398
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KEYWORDS
Image fusion
Feature fusion
Convolution
Image enhancement
Reconstruction algorithms
Feature extraction
Image processing
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