Image Enhancement Based on Light-Curve and Color Decoupling Techniques - Liang, Chengyu (International Journal of Computer Science and Information Technology, 2024)
This paper presents a lightweight, unsupervised image enhancement method based on existing models, termed LCCDnet (Light-curve and Color-decoupled). The method separates the enhancement task into light enhancement and detail optimization. Images are decomposed into HSV channels: HS channels manage image details, while the V channel controls brightness. A light curve is applied to estimate brightness enhancement, with design considerations for pixel value range, monotonicity, and differentiability. For HS channels, a method inspired by the Purkinje effect decomposes the channel into two intrinsic color values, which are then combined into a novel HS channel. LCCDnet does not require paired or unpaired data for training. The method achieves image enhancement through intuitive nonlinear curve mapping, focusing on image details. It is computationally efficient and requires minimal training data, showing promising potential despite some limitations in certain scenarios.