基于自适应退化感知自提示模型的一体化天气退化图像恢复
All-in-one Weather-degraded Image Restoration via Adaptive Degradation-aware Self-prompting Model
AI总结：
针对一体化天气退化图像恢复中退化感知先验利用低效的问题，提出自适应退化感知自提示模型ADSM，利用CLIP生成潜在提示并融入扩散模型，结合小波噪声估计网络，在八个数据集上验证了有效性。
摘要：
现有的一体化天气退化图像恢复方法在利用退化感知先验方面存在效率低下的问题，导致在适应不同天气条件时性能次优。为此，我们开发了一种用于一体化天气退化图像恢复的自适应退化感知自提示模型（ADSM）。具体而言，该模型采用对比语言-图像预训练模型（CLIP）来促进所提出的潜在提示生成器（LPG）的训练，LPG表示三种类型的潜在提示，用于表征退化类型、退化属性和图像描述。此外，我们将获取的退化感知提示整合到扩散模型的时间嵌入中以改善退化感知。同时，我们利用潜在描述提示，通过交叉注意力机制引导反向采样过程，从而引导准确的图像重建。此外，为加速扩散模型的反向采样过程并解决频率感知的局限性，我们引入了面向小波的噪声估计网络（WNE-Net）。在八个公开数据集上的大量实验表明，所提方法在特定任务和一体化应用中均具有有效性。
英文摘要：
Existing approaches for all-in-one weather-degraded image restoration suffer from inefficiencies in leveraging degradation-aware priors, resulting in sub-optimal performance in adapting to different weather conditions. To this end, we develop an adaptive degradation-aware self-prompting model (ADSM) for all-in-one weather-degraded image restoration. Specifically, our model employs the contrastive language-image pre-training model (CLIP) to facilitate the training of our proposed latent prompt generators (LPGs), which represent three types of latent prompts to characterize the degradation type, degradation property and image caption. Moreover, we integrate the acquired degradation-aware prompts into the time embedding of diffusion model to improve degradation perception. Meanwhile, we employ the latent caption prompt to guide the reverse sampling process using the cross-attention mechanism, thereby guiding the accurate image reconstruction. Furthermore, to accelerate the reverse sampling procedure of diffusion model and address the limitations of frequency perception, we introduce a wavelet-oriented noise estimating network (WNE-Net). Extensive experiments conducted on eight publicly available datasets demonstrate the effectiveness of our proposed approach in both task-specific and all-in-one applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
