DCT-FANet: DCT based frequency attention network for single image super-resolution

Abstract

In single-image super-resolution (SISR) task, it is challenging to recover high-frequency details from a low-resolution (LR) image due to its ill-posed problem. Most existing CNN-based super-resolution (SR) methods pursue an end-to-end solution from the low-resolution image to the high-resolution image through deeper or wider networks. However, these methods treat high-frequency information and low-frequency information equally which hinder the recovery of high-frequency details and result in low visual comfort. In this paper, we propose a DCT based frequency attention network (DCT-FANet) to distinguish different frequency of LR images, and enhance the high-frequency information adaptively. Specially, a DCT spatial cube extraction (DCT-SCE) module is proposed to decompose LR images into multiple frequency subbands. Different from other DCT based SISR methods that use DCT coefficients as subbands, our module considers the feature difference between spatial domain and frequency domain, and remains spatial structure information of each DCT subband. Then, based on the characteristics of DCT subbands, we design a Gaussian based frequency selection (GFS) module to give more attention to the high-frequency information. To promote communication between each frequency subband, an adaptive non-local dual attention (ANDA) module is developed, leading to a further enhancement of high-frequency information and improvement of the final performance. Experimental results demonstrates that our DCT-FANet recovers more high-frequency details, and achieves excellent performance with fewer parameters.