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Multistage supervised contrastive learning for hybrid-degraded image restoration


Natural image degradation is frequently unavoidable for various reasons, including noise, blur, compression artifacts, haze, and raindrops. The majority of previous works have advanced significantly. They, however, consider only one type of degradation and overlook hybrid degradation factors, which are fairly common in natural images. To tackle this challenge, we propose a multistage network architecture. It is capable of gradually learning and restoring the hybrid degradation model of the image. The model comprises three stages, with each pair of adjacent stages combining to exchange information between the early and late stages. Meanwhile, we employ a double-pooling channel attention block that combines maximum and average pooling. It is capable of inferring more intricate channel attention and enhancing the network’s representation capability. Then, during the model training step, we introduce contrastive learning. Our method outperforms comparable methods in terms of qualitative scores and visual effects and restores more detailed textures to improve image quality.

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This work was supported by the General project of Liaoning Provincial Department of Education, China, No. LJKZ0986; Postdoctoral Science Foundation, No. 2019M651123; Science and Technology Innovation Fund (Youth Science and Technology Star) of Dalian, China, No. 2018RQ65; receiver: Dr. Bo Fu. This work is supported by the National Natural Science Foundation of China (NSFC) Grant No.61976109, China; Liaoning Provincial Key Laboratory Special Fund; Dalian Key Laboratory Special Fund. Dr. Yonggong Ren. This research was funded by the University of Economics Ho Chi Minh City, Vietnam. Fund receiver: Dr. Dang Ngoc Hoang Thanh.

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Correspondence to Yonggong Ren or Dang N. H. Thanh.

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Fu, B., Dong, Y., Fu, S. et al. Multistage supervised contrastive learning for hybrid-degraded image restoration. SIViP 17, 573–581 (2023).

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