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|a 10.1109/TIP.2023.3289049
|2 doi
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|a pubmed24n1195.xml
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|a DE-627
|b ger
|c DE-627
|e rakwb
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|a eng
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| 100 |
1 |
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|a Chen, Wenshu
|e verfasserin
|4 aut
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| 245 |
1 |
0 |
|a TSDN
|b Two-Stage Raw Denoising in the Dark
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| 264 |
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|c 2023
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| 336 |
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|a Text
|b txt
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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| 500 |
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|a Date Completed 09.07.2023
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|a Date Revised 18.07.2023
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Denoising is one of the most significant procedures in the image processing pipeline. Nowadays, deep-learning-based algorithms have achieved superior denoising quality than traditional algorithms. However, the noise becomes severe in the dark environment, where even the SOTA algorithms fail to achieve satisfactory performance. Besides, the high computational complexity of deep-learning-based denoising algorithms makes them hardware unfriendly and difficult to process high-resolution images in real-time. To address these issues, a novel low-light RAW denoising algorithm Two-Stage-Denoising (TSDN), is proposed in this paper. In TSDN, denoising consists of two procedures: noise removal and image restoration. Firstly, in the noise-removal stage, most noise is removed from the image, and an intermediate image that is easier for the network to recover the clean image is obtained. Then, in the restoration stage, the clean image is restored from the intermediate image. The TSDN is designed to be light-weight for real-time and hardware friendly. However, the tiny network will be insufficient for satisfactory performance if directly trained from scratch. Therefore, we present an Expand-Shrink-Learning (ESL) method to train the TSDN. In the ESL method, firstly, the tiny network is expanded to a larger one with similar architecture but more channels and layers, which enhances the learning ability of the network because of more parameters. Secondly, the larger network is shrunk and restored to the original small network in fine-grained learning procedures, including Channel-Shrink-Learning (CSL) and Layer-Shrink-Learning (LSL). Experimental results demonstrate that the proposed TSDN achieves better performance (PSNR and SSIM) than other SOTA algorithms in the dark environment. Besides, the model size of TSDN is one-eighth of that of the U-Net for denoising (a classical denoising network)
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|a Journal Article
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| 700 |
1 |
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|a Huang, Yujie
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wang, Mingyu
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Wu, Xiaolin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Zeng, Xiaoyang
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 32(2023) vom: 28., Seite 3679-3689
|w (DE-627)NLM09821456X
|x 1941-0042
|7 nnns
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| 773 |
1 |
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|g volume:32
|g year:2023
|g day:28
|g pages:3679-3689
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|u http://dx.doi.org/10.1109/TIP.2023.3289049
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