论文标题
通过基于斑块的DeNoRising扩散模型在不利天气条件下恢复视力
Restoring Vision in Adverse Weather Conditions with Patch-Based Denoising Diffusion Models
论文作者
论文摘要
在不利天气条件下的图像恢复对各种计算机视觉应用引起了重大兴趣。最近的成功方法依赖于深度神经网络架构设计（例如，具有视觉变压器）的当前进展。由最新的条件生成模型取得的最新进展的动机，我们提出了一种基于斑块的图像恢复算法，基于脱氧扩散概率模型。我们的基于贴片的扩散建模方法可以通过使用指导的DeNoising过程进行尺寸 - 敏锐的图像恢复，并在推理过程中对重叠贴片进行平滑的噪声估计。我们在基准数据集上经验评估了我们的模型，以进行图像，合并的降低和脱掩护以及去除雨滴。我们展示了我们在特定天气和多天气图像恢复上实现最新性能的方法，并在实验上表现出对现实世界测试图像的强烈概括。
Image restoration under adverse weather conditions has been of significant interest for various computer vision applications. Recent successful methods rely on the current progress in deep neural network architectural designs (e.g., with vision transformers). Motivated by the recent progress achieved with state-of-the-art conditional generative models, we present a novel patch-based image restoration algorithm based on denoising diffusion probabilistic models. Our patch-based diffusion modeling approach enables size-agnostic image restoration by using a guided denoising process with smoothed noise estimates across overlapping patches during inference. We empirically evaluate our model on benchmark datasets for image desnowing, combined deraining and dehazing, and raindrop removal. We demonstrate our approach to achieve state-of-the-art performances on both weather-specific and multi-weather image restoration, and experimentally show strong generalization to real-world test images.
