Dark and Bright Channel Prior Embedded Network for Dynamic Scene Deblurring

Recent years have witnessed the significant progress on convolutional neural networks (CNNs) in dynamic scene deblurring. While most of the CNN models are generally learned by the reconstruction loss defined on training data, incorporating suitable image priors as well as regularization terms into the network architecture could boost the deblurring performance. In this work, we propose a Dark and Bright Channel Priors embedded Network (DBCPeNet) to plug the channel priors into a neural network for effective dynamic scene deblurring. A novel trainable dark and bright channel priors embedded layer (DBCPeL) is developed to aggregate both channel priors and blurry image representations, and a sparse regularization is introduced to regularize the DBCPeNet model learning. Furthermore, we present an effective multi-scale network architecture, namely image full scale exploitation (IFSE), which works in both coarse-to-fine and fine-to-coarse manners for better exploiting information flow across scales. Experimental results on the GoPro and Köhler datasets show that our proposed DBCPeNet performs favorably against state-of-the-art deep image deblurring methods in terms of both quantitative metrics and visual quality.

I. Introduction

Reproducing the visual richness of a real-world scene is an essential goal of digital photography. The real images, however, are often blurred during image acquisition due to the effect of many factors such as camera shake, object motion, and out-of-focus [1]. The resulting blurry images will not only degrade the perceptual quality of photos but also degenerate the performance of many image analytic and understanding models [2]. Blind image deblurring, which has been studied extensively in low level vision for decades [3], plays an essential role in improving the visual quality of real-world blurry images.