Abstract: Blind image super-resolution reconstruction aims to restore clear high-resolution images from blurred and degraded images in real-world scenarios. Although deep learning-based reconstruction methods have achieved some progress, the degradation models they rely on still have certain limitations. First, the blurring and noise-adding operations in the degradation process lack adaptability; second, the simulation of the degradation process is insufficient. To address these issues, this paper proposes a hybrid-order adaptive multi-dimensional degradation model. The model employs a hybrid-order degradation approach overall, consisting of two stages. The first stage is the adaptive degradation stage, which utilizes dynamic convolution to perform adaptive blurring and noise addition on high-resolution images. The second stage is the multi-dimensional degradation stage, which further processes the images generated in the first stage through distortion, brightness adjustment, rotation, and down-sampling. The proposed degradation model is integrated with classical super-resolution reconstruction networks to develop a blind image super-resolution reconstruction algorithm based on the hybrid-order adaptive multi-dimensional degradation model. To verify the effectiveness of the proposed method, comparative experiments were conducted on the Set14, BSD100, and DRealSR datasets. The results show that, compared to the PDM-SRGAN baseline method, the proposed method achieves improvements in peak signal-to-noise ratio (PSNR) of 0.84 dB, 0.63 dB, and 1.06 dB on the three datasets, respectively, in 4× super-resolution reconstruction tasks. This demonstrates that the proposed degradation model can effectively enhance the reconstruction performance and real-world adaptability of super-resolution algorithms, enabling the generation of higher-quality images.

摘要： 盲图像超分辨率重建旨在真实的场景下从模糊的退化图像中恢复得到清晰的高分辨率图像。尽管基于深度学习的重建方法取得了一定进展，但其所依赖的退化模型还存在一定的局限性。一是退化过程中的模糊和加噪操作缺乏自适应性，二是退化过程的模拟方式不够充分。针对以上问题，该文提出了一种混合阶自适应多维度退化模型。该模型整体使用混合阶的退化方式，并分为两个阶段。第一阶段为自适应退化阶段，利用动态卷积对高分辨率图像进行自适应模糊和添加噪声；第二阶段为多维度退化阶段，对第一阶段生成的图像做进一步失真、亮度调整、旋转和降采样的处理。将所提退化模型与经典超分辨率重建网络进行融合，提出一种基于混合阶自适应多维度退化模型的盲图像超分辨率重建算法。为验证所提方法的有效性，在Set14、BSD100和DRealSR数据集上开展对比实验，结果表明，相比PDM-SRGAN基准方法，本文所提方法在4倍超分辨率重建任务中，峰值信噪比指标在上述三个数据集上分别提升0.84 dB、0.63 dB和1.06 dB，表明所提退化模型可有效提升超分辨率算法的重建性能与真实场景适应性，使其能够生成更高质量的图像。