While single task image restoration (IR) has achieved significant successes,
it remains a challenging issue to train a single model which can tackle
multiple IR tasks. In this work, we investigate in-depth the multiple-in-one
(MiO) IR problem, which comprises seven popular IR tasks. We point out that MiO
IR faces two pivotal challenges: the optimization of diverse objectives and the
adaptation to multiple tasks. To tackle these challenges, we present two simple
yet effective strategies. The first strategy, referred to as sequential
learning, attempts to address how to optimize the diverse objectives, which
guides the network to incrementally learn individual IR tasks in a sequential
manner rather than mixing them together. The second strategy, i.e., prompt
learning, attempts to address how to adapt to the different IR tasks, which
assists the network to understand the specific task and improves the
generalization ability. By evaluating on 19 test sets, we demonstrate that the
sequential and prompt learning strategies can significantly enhance the MiO
performance of commonly used CNN and Transformer backbones. Our experiments
also reveal that the two strategies can supplement each other to learn better
degradation representations and enhance the model robustness. It is expected
that our proposed MiO IR formulation and strategies could facilitate the
research on how to train IR models with higher generalization capabilities.

本研究探讨了多个任务的图像恢复问题，提出了解决不同目标优化和适应性的策略，即顺序学习和提示学习，在 19 个测试集上得出了这些策略能够显著提高普通 CNN 和 Transformer 模型的综合性能，为训练具有更高泛化能力的图像恢复模型提供了新的理论和方法。