基于IB-UNet的零参考低照度图像增强方法

A Zero Reference Low Illumination Image Enhancement Method Based on IB-UNet

在图像增强领域,成对数据的过分依赖可能导致模型过度拟合,影响其泛化能力。为了解决这一问题,本文提出了一种无监督学习方法,该方法受到Zero-DCE网络架构的启发,采用了基于IB-UNet的零参考图像增强方法。该方法直接学习图像的深层特征,提高了图像特征纹理的提取效率,从而在不依赖成对参考数据的情况下,有效提升低照度图像的增强质量。通过客观指标评估,结合不同模型的对比试验与消融实验,客观验证了所提出模型的优势,展示了其在图像增强任务中的潜力和实用性。

In the field of image enhancement,an over-reliance on paired data can lead to overfitting of the model,which affects its generalization ability.To address this issue,this paper proposes an unsupervised learning method inspired by the Zero-DCE network architecture,which employs a zero-reference image enhancement method based on IB-UNet.This method directly learns the deep features of the image,improving the efficiency of extracting image feature textures,thereby effectively enhancing the quality of low-illumination images without relying on paired reference data.Through objective metrics evaluation,combined with comparative experiments of different models and ablation experiments,the advantages of the proposed model are objectively verified,de-monstrating its potential and practicality in the task of image enhancement.