Estimation and Restoration of Compositional Degradation Using Convolutional Neural Networks
This addresses the challenge of compositional image degradation in practical applications, representing an incremental improvement over single-degradation methods.
The paper tackles the problem of restoring images with mixed degradation types by proposing a CNN model to estimate degradation properties and a restoration CNN that uses these estimates, achieving successful blind and nonblind image restoration.
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.