Relationship Quantification of Image Degradations

In this paper, we study two challenging but less-touched problems in image restoration, namely, i) how to quantify the relationship between image degradations and ii) how to improve the performance of a specific restoration task using the quantified relationship. To tackle the first challenge, we propose the Degradation Relationship Index (DRI), which is defined as the mean drop rate difference in validation loss between two models, where one trained solely with anchor degradation and the other trained with both anchor and auxiliary degradations. By quantifying degradation relationship using DRI, we reveal that i) a positive DRI consistently indicates performance improvement when a beneficial auxiliary degradation is incorporated during training; ii) the proportion of auxiliary degradation is crucial to the anchor task performance. In other words, performance improvement is achieved only when the anchor and auxiliary degradations are combined in an appropriate proportion. Based on these observations, we further propose a simple yet effective Degradation Proportion Determination (DPD) method to estimate whether a given degradation combinations can enhance performance on the anchor restoration task with the assistance of auxiliary degradation. Extensive experimental results verify the effectiveness and generalizability of our method on noise, rain streak, haze and snow.