BUFF：用于单图像超分辨率的贝叶斯不确定性引导扩散概率模型
BUFF: Bayesian Uncertainty Guided Diffusion Probabilistic Model for Single Image Super-Resolution
AI总结：
本文提出贝叶斯不确定性引导扩散概率模型（BUFF），通过贝叶斯网络生成高分辨率不确定性掩码自适应调整扩散过程的噪声强度，有效提升了单图像超分辨率的保真度并减轻复杂纹理区域的伪影与模糊。
摘要：
超分辨率（SR）技术对于提升图像质量至关重要，特别是在高分辨率图像不可或缺却受限于硬件约束的场景中。现有的用于SR的扩散模型主要依赖高斯模型进行噪声生成，在处理自然场景中固有的复杂多变纹理时往往表现不足。为了解决这些缺陷，我们引入了贝叶斯不确定性引导扩散概率模型（BUFF）。BUFF的突出之处在于结合了贝叶斯网络来生成高分辨率不确定性掩码。这些掩码引导扩散过程，允许以既具备上下文感知又具备自适应的方式调整噪声强度。这种新颖的方法不仅提高了超分辨率图像对其原始高分辨率对应图像的保真度，还显著减轻了在具有复杂纹理和精细细节特征区域中的伪影和模糊。该模型对复杂噪声模式表现出卓越的鲁棒性，并在处理图像中的纹理和边缘方面展现出优异的适应性。有视觉结果支持的经验证据说明了该模型的鲁棒性，尤其是在具有挑战性的场景中，以及其在解决模糊等常见SR问题上的有效性。在DIV2K数据集上进行的实验评估表明，BUFF取得了显著的改进，在BSD100上的SSIM相比基线提高了+0.61，超越了传统扩散方法，平均额外获得了+0.20dB的PSNR增益。这些发现突显了贝叶斯方法在增强SR扩散过程方面的潜力，为该领域的未来进展铺平了道路。
英文摘要：
Super-resolution (SR) techniques are critical for enhancing image quality, particularly in scenarios where high-resolution imagery is essential yet limited by hardware constraints. Existing diffusion models for SR have relied predominantly on Gaussian models for noise generation, which often fall short when dealing with the complex and variable texture inherent in natural scenes. To address these deficiencies, we introduce the Bayesian Uncertainty Guided Diffusion Probabilistic Model (BUFF). BUFF distinguishes itself by incorporating a Bayesian network to generate high-resolution uncertainty masks. These masks guide the diffusion process, allowing for the adjustment of noise intensity in a manner that is both context-aware and adaptive. This novel approach not only enhances the fidelity of super-resolved images to their original high-resolution counterparts but also significantly mitigates artifacts and blurring in areas characterized by complex textures and fine details. The model demonstrates exceptional robustness against complex noise patterns and showcases superior adaptability in handling textures and edges within images. Empirical evidence, supported by visual results, illustrates the model's robustness, especially in challenging scenarios, and its effectiveness in addressing common SR issues such as blurring. Experimental evaluations conducted on the DIV2K dataset reveal that BUFF achieves a notable improvement, with a +0.61 increase compared to baseline in SSIM on BSD100, surpassing traditional diffusion approaches by an average additional +0.20dB PSNR gain. These findings underscore the potential of Bayesian methods in enhancing diffusion processes for SR, paving the way for future advancements in the field.
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