期刊
IEEE TRANSACTIONS ON MULTIMEDIA
卷 24, 期 -, 页码 2366-2377出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TMM.2021.3079697
关键词
Noise reduction; Noise measurement; Image denoising; Feature extraction; Adaptation models; Process control; Deep learning; Image denoising; Real-world Noisy Image; LSTM; PID Controller
资金
- University of Macau [MYRG2019-00006-FST]
- National Natural Science Foundation of China [61602540]
- Youth Innovation Project of the Department of Education of Guangdong Province [2020KQNCX040]
With the development of deep learning technologies, the proposed HPDNet achieves a considerable improvement in performance for real-world noisy image denoising, providing a balanced trade-off between denoising accuracy and efficiency.
With the development of deep learning technologies, recent research on real-world noisy image denoising has achieved a considerable improvement in performance. However, a common limitation for existing approaches is the imbalanced trade-off between denoising accuracy and efficiency. To address this problem, we propose a robust and efficient denoiser, called a hierarchical-based PID-attention denoising network (HPDNet), to flexibly deal with the sophisticated noise. The core of our algorithm is the PID-attentive recurrent network (PAR-Net) whose framework mainly consists of the LSTM network and PID controller. PAR-Net inherits the advantages of both the attentive recurrent network and control action, which can encourage more discriminatory feature representations. This learning procedure is implemented within a feedback control system, allowing a faster and more robust means to enhance feature discriminability. Furthermore, by decomposing the noisy image and stacking the PAR-Nets, our PAR-Net can work on a progressively hierarchical framework, and hence obtain multi-scale features and manageable successive refinements. On several widely used datasets, the proposed HPDNet demonstrates high efficiency, while delivering a better perceptually appealing image quality over state-of-the-art image denoising methods.
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