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 has not been exploited in previous works), which drastically
diminishes computation cost. We then define the unsupervised training loss to
elevate the model capability that can adapt to general scenes. Further, we make
comprehensive explorations to excavate SCI's inherent properties (lacking in
existing works) including operation-insensitive adaptability (acquiring stable
performance under the settings of different simple operations) and
model-irrelevant generality (can be applied to illumination-based existing
works to improve performance). Finally, plenty of experiments and ablation
studies fully indicate our superiority in both quality and efficiency.
Applications on low-light face detection and nighttime semantic segmentation
fully reveal the latent practical values for SCI. The source code is available
at this https URL

本文提出一种新的自校准照明（SCI）学习框架，用于在低光环境下快速，灵活和准确地增强图像，通过耦合权值共享的级联照明学习过程来实现此任务，从而极大地减少计算成本，提高模型适用性。