Low-light Stereo Image Enhancement and De-noising in the Low-frequency Information Enhanced Image Space
This addresses the problem of enhancing low-light stereo images with complex noise for applications like computer vision, though it is incremental as it builds on existing stereo enhancement methods.
The paper tackles low-light stereo image enhancement and denoising by proposing a method that simultaneously enhances images and reduces noise, using a low-frequency information enhanced module and cross-view interactions, achieving better detail recovery and noise removal compared to state-of-the-art methods on synthesized and real datasets.
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.