DaLPSR：利用退化对齐语言提示实现真实世界图像超分辨率
DaLPSR: Leverage Degradation-Aligned Language Prompt for Real-World Image Super-Resolution
AI总结：
本文提出DaLPSR方法，利用退化对齐语言提示结合语义与退化先验，通过提示对齐解码器和多模态大语言模型实现真实世界图像超分辨率的高保真恢复，并在多个基准上达到新的最先进感知质量。
摘要：
图像超分辨率旨在为低分辨率图像重建高保真的高分辨率对应图像。近年来，基于扩散的模型因其丰富的先验知识能力而受到广泛关注。基于通用文本提示的扩散模型的成功验证了文本控制在文本到图像领域中的有效性。然而，鉴于低分辨率图像中普遍存在的严重退化，加之扩散模型的随机性特征，当前模型难以充分辨识严重退化图像中的语义和退化信息。这往往导致语义丢失、视觉伪影和视觉幻觉等障碍，给实际应用带来重大挑战。为解决这些挑战，本文提出利用退化对齐语言提示来实现准确、细粒度且高保真的图像恢复。研究探索了包括语义内容描述和退化提示在内的互补先验。具体而言，一方面，提出了图像恢复提示对齐解码器，用于自动辨识低分辨率图像的退化程度，从而生成有利于图像恢复的退化先验。另一方面，来自预训练多模态大语言模型的丰富定制描述引出与人类感知高度一致的高层语义先验，确保图像恢复的保真度控制。在多个流行的合成和真实世界基准数据集上与最先进方法进行了全面比较。定量和定性分析表明，所提方法达到了新的最先进感知质量水平。相关源代码和预训练参数已在 https://github.com/puppy210/DaLPSR 公开。
英文摘要：
Image super-resolution pursuits reconstructing high-fidelity high-resolution counterpart for low-resolution image. In recent years, diffusion-based models have garnered significant attention due to their capabilities with rich prior knowledge. The success of diffusion models based on general text prompts has validated the effectiveness of textual control in the field of text2image. However, given the severe degradation commonly presented in low-resolution images, coupled with the randomness characteristics of diffusion models, current models struggle to adequately discern semantic and degradation information within severely degraded images. This often leads to obstacles such as semantic loss, visual artifacts, and visual hallucinations, which pose substantial challenges for practical use. To address these challenges, this paper proposes to leverage degradation-aligned language prompt for accurate, fine-grained, and high-fidelity image restoration. Complementary priors including semantic content descriptions and degradation prompts are explored. Specifically, on one hand, image-restoration prompt alignment decoder is proposed to automatically discern the degradation degree of LR images, thereby generating beneficial degradation priors for image restoration. On the other hand, much richly tailored descriptions from pretrained multimodal large language model elicit high-level semantic priors closely aligned with human perception, ensuring fidelity control for image restoration. Comprehensive comparisons with state-of-the-art methods have been done on several popular synthetic and real-world benchmark datasets. The quantitative and qualitative analysis have demonstrated that the proposed method achieves a new state-of-the-art perceptual quality level. Related source codes and pre-trained parameters were public in https://github.com/puppy210/DaLPSR.
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。
