Lightweight Image Deblurring via Recurrent Gated Attention and Efficient Decoupling

In recent years, deep learning has been significantly advancing the field of image deblurring. However, existing deep learning models usually rely on overloaded large kernel convolutions or overweighted attention modules. This leads to a heavy computational burden and restricts real applications. To address this issue, we propose a lightweight deblurring network, termed RGE-Net. Our RGE-Net possesses two novel features: 1) We propose a recurrent path into the convolutions to ensure each kernel weight can learn better and stronger feature information, thus increasing the parameter efficiency and reducing the parameters. Furthermore, we propose gated attention to suppress incorrect features flowing in the recurrent path, thus improving performance. 2) We decouple the kernels into spatial and channel components to reduce learning difficulty by reducing parameters and then perform an attention mechanism to obtain significant performance. Extensive experiments on benchmark datasets demonstrate the superiority of RGE-Net over state-of-the-art deblurring models in terms of both effectiveness and efficiency.