Forest nighttime image enhancement based on improved SCI light estimation and reflection optimization - Xian Zhang; Yanfeng Li; Hanyue Zhang (IET Image Processing, 2024)
Abstract The images captured by forest nighttime imaging devices are prone to degradation and noise issues due to environmental constraints. This paper proposes a novel framework based on the self‐calibrating illumination framework to improve image quality. The framework consists of four specific steps: (1) addressing non‐uniform degradation by estimating illuminance components through non‐uniform MaxRGB, (2) extracting rich information using multi‐scale inflationary convolution, (3) analyzing the relationship between different input levels and the initial level and progressively adjusting the luminance components using a channel self‐calibration module to achieve continuous improvement in luminance, and (4) designing a reflectance optimization module that enhances illuminance while suppressing noise through a regularization model. To evaluate the proposed method, a dataset comprising 1000 forest night images is constructed using an image simulation strategy. The effectiveness of this method was validated on both synthetic and real datasets, demonstrating its ability to significantly enhance nighttime forest images. When compared to six other algorithms, this method outperformed them in eight evaluation metrics, including a peak signal‐to‐noise ratio metric of 22.839 and a structural similarity metric of 0.909. The experimental results clearly indicate that this method excels in enhancing image quality and fidelity.