Abstract
Images captured in challenging environments often experience various forms of degradation, including noise, color cast, blur, and light scattering. These effects significantly reduce image quality, hindering their applicability in downstream tasks such as object detection, mapping, and classification. Our transformer-based diffusion model was developed to address image restoration tasks, aiming to improve the quality of degraded images. This model was evaluated against existing deep learning methodologies across multiple quality metrics for underwater image enhancement, denoising, and deraining on publicly available datasets. Our findings demonstrate that the diffusion model, combined with transformers, surpasses current methods in performance. The results of our model highlight the efficacy of diffusion models and transformers in improving the quality of degraded images, consequently expanding their utility in downstream tasks that require high-fidelity visual data.
Abstract (translated)
在具有挑战性的环境中捕获的图像经常会遭受各种形式的质量退化，包括噪点、色偏、模糊和光线散射。这些影响显著降低了图像质量，妨碍了它们在下游任务（如物体检测、地图绘制和分类）中的应用效果。我们开发了一种基于变压器的扩散模型来解决图像恢复任务，旨在提升受损图像的质量。我们的模型在公开可用的数据集上进行了评估，针对水下图像增强、去噪和除雨等任务，并与现有的深度学习方法进行了多方面的质量指标对比。研究结果表明，结合了扩散模型和变压器的方法在性能上超越了当前的其他方法。我们的模型成果突显了扩散模型和变压器在提升受损图像质量方面的能力，从而扩大其在需要高保真视觉数据的下游任务中的应用范围。
URL
https://arxiv.org/abs/2506.20302
