  In dynamic scenes, images often suffer from dynamic blur due to superposition
of motions or low signal-noise ratio resulted from quick shutter speed when
avoiding motions. Recovering sharp and clean results from the captured images
heavily depends on the ability of restoration methods and the quality of the
input. Although existing research on image restoration focuses on developing
models for obtaining better restored results, fewer have studied to evaluate
how and which input image leads to superior restored quality. In this paper, to
better study an image's potential value that can be explored for restoration,
we propose a novel concept, referring to image restoration potential (IRP).
Specifically, We first establish a dynamic scene imaging dataset containing
composite distortions and applied image restoration processes to validate the
rationality of the existence to IRP. Based on this dataset, we investigate
several properties of IRP and propose a novel deep model to accurately predict
IRP values. By gradually distilling and selective fusing the degradation
features, the proposed model shows its superiority in IRP prediction. Thanks to
the proposed model, we are then able to validate how various image restoration
related applications are benefited from IRP prediction. We show the potential
usages of IRP as a filtering principle to select valuable frames, an auxiliary
guidance to improve restoration models, and even an indicator to optimize
camera settings for capturing better images under dynamic scenarios.
