In this paper, we study two challenging but less-touched problems in image restoration, namely, i) how to quantify the relationship between different image degradations and ii) how to improve the performance on a specific degradation using the quantified relationship. To tackle the first challenge, Degradation Relationship Index (DRI) is proposed to measure the degradation relationship, which is defined as the mean drop rate difference in the validation loss between two models, i.e., one is trained using the anchor degradation only and another is trained based on both the anchor and the auxiliary degradations. Through quantifying the relationship between different degradations using DRI, we empirically observe that i) the degradation combination proportion is crucial to the image restoration performance. In other words, the combinations with only appropriate degradation proportions could improve the performance of the anchor restoration; ii) a positive DRI always predicts the performance improvement of image restoration. Based on the observations, we propose an adaptive Degradation Proportion Determination strategy (DPD) which could improve the performance on the anchor degradation with the assist of another auxiliary degradation. Extensive experimental results verify the effective of our method by taking haze as the anchor degradation and noise, rain streak, and snow as the auxiliary degradations. The code will be released after acceptance.

翻译：本文研究了图像恢复中两个具有挑战性但较少被探讨的问题，即：i) 如何量化不同图像退化之间的关系，以及ii)如何利用量化关系提升特定退化任务的性能。针对第一个挑战，我们提出退化关系指数（Degradation Relationship Index, DRI），通过计算两个模型在验证损失上的平均下降率差异来度量退化关系——其中一个模型仅使用锚点退化训练，另一个则基于锚点退化和辅助退化联合训练。通过DRI对不同退化间关系进行量化，我们实证发现：i) 退化组合比例对图像恢复性能至关重要，即只有包含适当退化比例的组合才能提升锚点恢复的性能；ii) 正向DRI始终预示着图像恢复性能的提升。基于这些发现，我们提出自适应退化比例确定策略（DPD），该策略能在辅助退化的帮助下提升锚点退化的恢复性能。以雾霾作为锚点退化，噪声、雨痕和雪作为辅助退化的广泛实验验证了本方法的有效性。代码将在论文接收后开源。