Toward Fast, Flexible, and Robust Low-Light Image Enhancement

Existing low-light image enhancement techniques are mostly not only difficult to deal with both visual quality and computational efficiency but also commonly invalid in unknown complex scenarios. In this paper, we develop a new Self-Calibrated Illumination (SCI) learning framework for fast, flexible, and robust brightening images in real-world low-light scenarios. To be specific, we establish a cascaded illumination learning process with weight sharing to handle this task. Considering the computational burden of the cascaded pattern, we construct the self-calibrated module which realizes the convergence between results of each stage, producing the gains that only use the single basic block for inference (yet…

• NN
  National Natural Science Foundation of China

  Award: 61922019,61733002,62027826
• NK
  National Key Research and Development Program of China

  Award: 2020YF-B1313503
• FR
  Fundamental Research Funds for the Central Universities