Image Deblurring Using Feedback Mechanism and Dual Gated Attention Network

Recently, image deblurring task driven by the encoder-decoder network has made a tremendous amount of progress. However, these encoder-decoder-based networks still have two disadvantages: (1) due to the lack of feedback mechanism in the decoder design, the reconstruction results of existing networks are still sub-optimal; (2) these networks introduce multiple modules, such as the self-attention mechanism, to improve the performance, which also increases the computational burden. To overcome these issues, this paper proposes a novel feedback-mechanism-based encoder-decoder network (namely, FMNet) that is equipped with two key components: (1) the feedback-mechanism-based decoder and (2) the dual gated attention module. To improve reconstruction quality, the feedback-mechanism-based decoder is proposed to leverage the feedback information via the feedback attention module, which adaptively selects useful features in the feedback path. To decrease the computational cost, an efficient dual gated attention module is proposed to perform the attention mechanism in the frequency domain twice, which improves deblurring performance while reducing the computational cost by avoiding redundant convolutions and feature channels. The superiority of FMNet in terms of both deblurring performance and computational efficiency is demonstrated via comparisons with state-of-the-art methods on multiple public datasets.