Image super-resolution (SR) research has witnessed impressive progress thanks to the advance of convolutional neural networks (CNNs) in recent years. However, most existing SR methods are non-blind and assume that degradation has a single fixed and known distribution (e.g., bicubic) which struggle while handling degradation in real-world data that usually follows a multi-modal, spatially variant, and unknown distribution. The recent blind SR studies address this issue via degradation estimation, but they do not generalize well to multi-source degradation and cannot handle spatially variant degradation. We design CRL-SR, a contrastive representation learning network that focuses on blind SR of images with multi-modal and spatially variant distributions. CRL-SR addresses the blind SR challenges from two perspectives. The first is contrastive decoupling encoding which introduces contrastive learning to extract resolution-invariant embedding and discard resolution-variant embedding under the guidance of a bidirectional contrastive loss. The second is contrastive feature refinement which generates lost or corrupted high-frequency details under the guidance of a conditional contrastive loss. Extensive experiments on synthetic datasets and real images show that the proposed CRL-SR can handle multi-modal and spatially variant degradation effectively under blind settings and it also outperforms state-of-the-art SR methods qualitatively and quantitatively.
翻译:图像超分辨率(SR)研究近年来由于神经神经网络(CNNs)的进步而取得了令人印象深刻的进展,然而,大多数现有的SR方法都是非盲的,并假定退化有一个单一的固定和已知分布(如双立方),而这种分布在处理现实世界数据退化时挣扎,而现实世界数据通常遵循的是多模式、空间变异和未知分布。最近的盲SR研究通过降解估计来解决这一问题,但并没有将这一问题广泛归纳为多源降解,无法处理空间变异退化。我们设计了一个CRL-SR,这是一个对比性代表学习网络,侧重于多模式和空间变异分布图像的盲式SR。CRL-SR从两个角度应对了盲型SR挑战。第一个是对比性脱钩编码,它引入了对比性学习,提取分辨率变异性嵌入和丢弃解变量嵌入双向对比性损失的指南。第二个是对比性改进性特征,在一个有条件对比性变异式的图像的引导下产生丢失或腐坏的高频度细节,在一个有条件的变异性SR结构下,并有效展示了真实的定性的变式图像。