ITRE: Low-light Image Enhancement Based on Illumination Transmission
  Ratio Estimation
Yu Wang Yihong Wang Tong Liu Xiubao Sui Qian Chen
Noise, artifacts, and over-exposure are significant challenges in the field
of low-light image enhancement. Existing methods often struggle to address
these issues simultaneously. In this paper, we propose a novel Retinex-based
method, called ITRE, which suppresses noise and artifacts from the origin of
the model, prevents over-exposure throughout the enhancement process.
Specifically, we assume that there must exist a pixel which is least disturbed
by low light within pixels of same color. First, clustering the pixels on the
RGB color space to find the Illumination Transmission Ratio (ITR) matrix of the
whole image, which determines that noise is not over-amplified easily. Next, we
consider ITR of the image as the initial illumination transmission map to
construct a base model for refined transmission map, which prevents artifacts.
Additionally, we design an over-exposure module that captures the fundamental
characteristics of pixel over-exposure and seamlessly integrate it into the
base model. Finally, there is a possibility of weak enhancement when
inter-class distance of pixels with same color is too small. To counteract
this, we design a Robust-Guard module that safeguards the robustness of the
image enhancement process. Extensive experiments demonstrate the effectiveness
of our approach in suppressing noise, preventing artifacts, and controlling
over-exposure level simultaneously. Our method performs superiority in
qualitative and quantitative performance evaluations by comparing with
state-of-the-art methods.