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Unlike single image task, stereo image enhancement can use another viewinformation, and its key stage is how to perform cross-view feature interactionto extract useful information from another view. However, complex noise inlow-light image and its impact on subsequent feature encoding and interactionare ignored by the existing methods. In this paper, a method is proposed toperform enhancement and de-noising simultaneously. First, to reduce unwantednoise interference, a low-frequency information enhanced module (IEM) isproposed to suppress noise and produce a new image space. Additionally, across-channel and spatial context information mining module (CSM) is proposedto encode long-range spatial dependencies and to enhance inter-channel featureinteraction. Relying on CSM, an encoder-decoder structure is constructed,incorporating cross-view and cross-scale feature interactions to performenhancement in the new image space. Finally, the network is trained with theconstraints of both spatial and frequency domain losses. Extensive experimentson both synthesized and real datasets show that our method obtains betterdetail recovery and noise removal compared with state-of-the-art methods. Inaddition, a real stereo image enhancement dataset is captured with stereocamera ZED2. The code and dataset are publicly available at:https://www.github.com/noportraits/LFENet.

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