Enhanced Image Deblurring: An Efficient Frequency Exploitation and Preservation Network

Most of these frequency-based deblurring methods mainly have two major limitations: (1) insufficient exploitation of frequency information, (2) inadequate preservation of frequency information. In this paper, we propose a novel Efficient Frequency Exploitation and Preservation Network (EFEP) to address these limitations. Firstly, we propose a novel Frequency-Balanced Exploitation Encoder (FBE-Encoder) to sufficiently exploit frequency information. We insert a novel Frequency-Balanced Navigator (FBN) module in the encoder, which establishes a dynamic balance that adaptively explores and integrates the correlations between frequency features and other features presented in the network. And it also can highlight the most important regions in frequency features. Secondly, considering the limitation that frequency information is inevitably lost in deep network architectures, we present an Enhanced Selective Frequency Decoder (ESF-Decoder) that not only effectively reduces spatial information redundancy, but also fully explores the different importance of various frequency information to ensure the supplement of valid spatial information and weaken the invalid information. Thirdly, each encoder/decoder block of the EFEP consists of multiple Contrastive Residual Blocks (CRBs), which are designed to explicitly compute and incorporate feature distinctions. Powered by the above designs, our EFEP outperforms state-of-the-art models on both quantitative and qualitative evaluations.