A Lightweight Low-Light Image Enhancement Network via Channel Prior and
  Gamma Correction
Shyang-En Weng Shaou-Gang Miaou Ricky Christanto
Human vision relies heavily on available ambient light to perceive objects.
Low-light scenes pose two distinct challenges: information loss due to
insufficient illumination and undesirable brightness shifts. Low-light image
enhancement (LLIE) refers to image enhancement technology tailored to handle
this scenario. We introduce CPGA-Net, an innovative LLIE network that combines
dark/bright channel priors and gamma correction via deep learning and
integrates features inspired by the Atmospheric Scattering Model and the
Retinex Theory. This approach combines the use of traditional and deep learning
methodologies, designed within a simple yet efficient architectural framework
that focuses on essential feature extraction. The resulting CPGA-Net is a
lightweight network with only 0.025 million parameters and 0.030 seconds for
inference time, yet it achieves superior performance over existing LLIE methods
on both objective and subjective evaluation criteria. Furthermore, we utilized
knowledge distillation with explainable factors and proposed an efficient
version that achieves 0.018 million parameters and 0.006 seconds for inference
time. The proposed approaches inject new solution ideas into LLIE, providing
practical applications in challenging low-light scenarios.