Contrastive learning has emerged as a prevailing paradigm for high-level vision tasks, which, by introducing properly negative samples, has also been exploited for low-level vision tasks to achieve a compact optimization space to account for their ill-posed nature. However, existing methods rely on manually predefined and task-oriented negatives, which often exhibit pronounced task-specific biases. To address this challenge, our paper introduces an innovative method termed 'learning from history', which dynamically generates negative samples from the target model itself. Our approach, named Model Contrastive paradigm for Image Restoration (MCIR), rejuvenates latency models as negative models, making it compatible with diverse image restoration tasks. We propose the Self-Prior guided Negative loss (SPN) to enable it. This approach significantly enhances existing models when retrained with the proposed model contrastive paradigm. The results show significant improvements in image restoration across various tasks and architectures. For example, models retrained with SPN outperform the original FFANet and DehazeFormer by 3.41 dB and 0.57 dB on the RESIDE indoor dataset for image dehazing. Similarly, they achieve notable improvements of 0.47 dB on SPA-Data over IDT for image deraining and 0.12 dB on Manga109 for a 4x scale super-resolution over lightweight SwinIR, respectively. Code and retrained models are available at https://github.com/Aitical/MCIR.

翻译：对比学习已成为高级视觉任务的主流范式，通过引入适当的负样本，也被用于低级视觉任务以构建紧凑的优化空间来应对其不适定性。然而，现有方法依赖人工预定义且面向任务的负样本，这些负样本往往表现出显著的任务特定偏差。为解决这一挑战，本文提出了一种名为"从历史中学习"的创新方法，该方法从目标模型自身动态生成负样本。我们的方法名为"图像复原的模型对比范式"（MCIR），将滞后模型重新激活为负模型，使其兼容多种图像复原任务。我们提出了自先验引导负损失（SPN）来实现这一目标。该方法在使用所提出的模型对比范式重新训练后，显著增强了现有模型。实验结果表明，该方法在各种任务和架构的图像复原中均取得了显著改进。例如，使用SPN重新训练的模型在RESIDE室内数据集上的图像去雾任务中，分别比原始FFANet和DehazeFormer高出3.41 dB和0.57 dB。类似地，它们在SPA-Data上比IDT的图像去雨任务提升0.47 dB，在Manga109上比轻量级SwinIR的4倍超分辨率任务提升0.12 dB。代码和重新训练的模型可在https://github.com/Aitical/MCIR获取。