Learning to restore multiple image degradations within a single model is
quite beneficial for real-world applications. Nevertheless, existing works
typically concentrate on regarding each degradation independently, while their
relationship has been less exploited to ensure the synergistic learning. To
this end, we revisit the diverse degradations through the lens of singular
value decomposition, with the observation that the decomposed singular vectors
and singular values naturally undertake the different types of degradation
information, dividing various restoration tasks into two groups,\ie, singular
vector dominated and singular value dominated. The above analysis renders a
more unified perspective to ascribe the diverse degradations, compared to
previous task-level independent learning. The dedicated optimization of
degraded singular vectors and singular values inherently utilizes the potential
relationship among diverse restoration tasks, attributing to the Decomposition
Ascribed Synergistic Learning (DASL). Specifically, DASL comprises two
effective operators, namely, Singular VEctor Operator (SVEO) and Singular VAlue
Operator (SVAO), to favor the decomposed optimization, which can be lightly
integrated into existing convolutional image restoration backbone. Moreover,
the congruous decomposition loss has been devised for auxiliary. Extensive
experiments on blended five image restoration tasks demonstrate the
effectiveness of our method, including image deraining, image dehazing, image
denoising, image deblurring, and low-light image enhancement.

学习如何在单个模型中恢复多个图像退化是实际应用中非常有益的；因此，本文通过奇异值分解重新审视多样的图像退化，并提出了一种基于奇异向量和奇异值优化的协同学习方法 —— 分解归因协同学习（DASL），以实现各种图像恢复任务之间的潜在关系的利用。通过将 DASL 轻松整合到现有的卷积图像恢复骨干中，我们的方法在融合了五个图像恢复任务的广泛实验中表现出了有效性。