Low-light Stereo Image Enhancement and De-noising in the Low-frequency
  Information Enhanced Image Space
Minghua Zhao Xiangdong Qin Shuangli Du Xuefei Bai Jiahao Lyu Yiguang Liu
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.