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CameraNet: A Two-Stage Framework for Effective Camera ISP Learning.
- Source :
-
IEEE Transactions on Image Processing . 2021, Vol. 30, p2248-2262. 15p. - Publication Year :
- 2021
-
Abstract
- Traditional image signal processing (ISP) pipeline consists of a set of cascaded image processing modules onboard a camera to reconstruct a high-quality sRGB image from the sensor raw data. Recently, some methods have been proposed to learn a convolutional neural network (CNN) to improve the performance of traditional ISP. However, in these works usually a CNN is directly trained to accomplish the ISP tasks without considering much the correlation among the different components in an ISP. As a result, the quality of reconstructed images is barely satisfactory in challenging scenarios such as low-light imaging. In this paper, we firstly analyze the correlation among the different tasks in an ISP, and categorize them into two weakly correlated groups: restoration and enhancement. Then we design a two-stage network, called CameraNet, to progressively learn the two groups of ISP tasks. In each stage, a ground truth is specified to supervise the subnetwork learning, and the two subnetworks are jointly fine-tuned to produce the final output. Experiments on three benchmark datasets show that the proposed CameraNet achieves consistently compelling reconstruction quality and outperforms the recently proposed ISP learning methods. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10577149
- Volume :
- 30
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Image Processing
- Publication Type :
- Academic Journal
- Accession number :
- 170077650
- Full Text :
- https://doi.org/10.1109/TIP.2021.3051486