DEANet: Decomposition Enhancement and Adjustment Network for Low-Light Image Enhancement
Yonglong Jiang, Liangliang Li, Jiahe Zhu, Yuan Xue, Hongbing Ma
Abstract
Poor illumination greatly affects the quality of obtained images. In this paper, a novel convolutional neural network named DEANet is proposed on the basis of Retinex for low-light image enhancement. DEANet combines the frequency and content information of images and is divided into three subnetworks: decomposition, enhancement, and adjustment networks, which perform image decomposition; denoising, contrast enhancement, and detail preservation; and image adjustment and generation, respectively. The model is trained on the public LOL dataset, and the experimental results show that it outperforms the existing state-of-the-art methods regarding visual effects and image quality.
Topics & Concepts
Image enhancementArtificial intelligenceComputer scienceContrast enhancementImage (mathematics)DecompositionComputer visionConvolutional neural networkImage qualityContrast (vision)Pattern recognition (psychology)Noise reductionColor constancyRadiologyMagnetic resonance imagingBiologyMedicineEcologyImage Enhancement TechniquesAdvanced Image Processing TechniquesAdvanced Image Fusion Techniques