  Image restoration from a single image degradation type, such as blurring,
hazing, random noise, and compression has been investigated for decades.
However, image degradations in practice are often a mixture of several types of
degradation. Such compositional degradations complicate restoration because
they require the differentiation of different degradation types and levels. In
this paper, we propose a convolutional neural network (CNN) model for
estimating the degradation properties of a given degraded image. Furthermore,
we introduce an image restoration CNN model that adopts the estimated
degradation properties as its input. Experimental results show that the
proposed degradation estimation model can successfully infer the degradation
properties of compositionally degraded images. The proposed restoration model
can restore degraded images by exploiting the estimated degradation properties
and can achieve both blind and nonblind image restorations.
