Low-light Stereo Image Enhancement and De-noising in the Low-frequency Information Enhanced Image Space

• URL: http://arxiv.org/abs/2401.07753v1
• Date: Mon, 15 Jan 2024 15:03:32 GMT
• Title: Low-light Stereo Image Enhancement and De-noising in the Low-frequency Information Enhanced Image Space
• Authors: Minghua Zhao, Xiangdong Qin, Shuangli Du, Xuefei Bai, Jiahao Lyu, Yiguang Liu
• Abstract summary: Methods are proposed to perform enhancement and de-noising simultaneously. Low-frequency information enhanced module (IEM) is proposed to suppress noise and produce a new image space. Cross-channel and spatial context information mining module (CSM) is proposed to encode long-range spatial dependencies. An encoder-decoder structure is constructed, incorporating cross-view and cross-scale feature interactions.
• Score: 5.1569866461097185
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unlike single image task, stereo image enhancement can use another view information, and its key stage is how to perform cross-view feature interaction to extract useful information from another view. However, complex noise in low-light image and its impact on subsequent feature encoding and interaction are ignored by the existing methods. In this paper, a method is proposed to perform enhancement and de-noising simultaneously. First, to reduce unwanted noise interference, a low-frequency information enhanced module (IEM) is proposed to suppress noise and produce a new image space. Additionally, a cross-channel and spatial context information mining module (CSM) is proposed to encode long-range spatial dependencies and to enhance inter-channel feature interaction. Relying on CSM, an encoder-decoder structure is constructed, incorporating cross-view and cross-scale feature interactions to perform enhancement in the new image space. Finally, the network is trained with the constraints of both spatial and frequency domain losses. Extensive experiments on both synthesized and real datasets show that our method obtains better detail recovery and noise removal compared with state-of-the-art methods. In addition, a real stereo image enhancement dataset is captured with stereo camera ZED2. The code and dataset are publicly available at: https://www.github.com/noportraits/LFENet.

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