用Transformer+扩散模型修复水下、去噪、去雨等退化图像
TDiR: Transformer based Diffusion for Image Restoration Tasks
- 基于Transformer架构的扩散模型，联合建模图像退化特征
- 在5个基准上超越18种现有方法，提升图像质量
- 适合需要高保真视觉数据的下游任务使用
在复杂环境下捕获的图像常出现噪声、色偏、模糊和光散射等多种退化问题，显著降低图像质量，影响目标检测、地图构建和分类等下游任务的效果。本文提出基于Transformer的扩散模型，用于解决图像修复挑战，提升退化图像质量。方法在水下增强、去噪和去雨三个主要任务上进行评估，采用五个标准基准，并与18种先进方法对比，使用四个评价指标。结果表明，结合Transformer的扩散模型优于现有方法。研究证实了扩散模型与Transformer在提升退化图像质量方面的有效性，拓展了其在对高保真视觉数据有需求的下游任务中的应用。
原文摘要 · Abstract (English)
Images captured in challenging environments often experience various types of degradation, such as noise, color cast, blur, and light scattering. These issues significantly lower image quality, thereby reducing their usefulness in downstream tasks such as object detection, mapping, and classification. Our transformer-based diffusion model was developed to address image restoration challenges and enhance the quality of degraded images. Our methodology is assessed across three primary image restoration tasks, including underwater enhancement, denoising, and deraining, utilizing five standard benchmarks. It is then compared to 18 state-of-the-art techniques, employing four evaluation metrics. Our results show that the diffusion model, combined with transformers, outperforms current methods. The findings highlight the effectiveness of diffusion models and transformers in improving degraded image quality, thereby broadening their application in downstream tasks that demand high-fidelity visual data
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