  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
proposed a Degradation Relationship Index (DRI) which is defined as the mean
drop rate difference in the validation loss between two models which are
respectively trained using the anchor degradation and the mixture of the anchor
and the auxiliary degradations. Through quantifying the degradation
relationship using DRI, we reveal that i) a positive DRI always predicts
performance improvement by using the specific degradation as an auxiliary to
train models; ii) the degradation proportion is crucial to the image
restoration performance. In other words, the restoration performance is
improved only if the anchor and the auxiliary degradations are mixed with an
appropriate proportion. Based on the observations, we further propose a simple
but effective method (dubbed DPD) to estimate whether the given degradation
combinations could improve the performance on the anchor degradation with the
assistance of the auxiliary degradation. Extensive experimental results verify
the effectiveness of our method in dehazing, denoising, deraining, and
desnowing. The code will be released after acceptance.
