Single Fundus Image Super-Resolution Via Cascaded Channel-Wise Attention Network.

Fundus image is commonly used in aiding the diagnosis of ophthalmic diseases. A high-resolution (HR) image is valuable to provide the anatomic information on the eye conditions. Recently, image super-resolution (SR) though learning model has been shown to be an economic yet effective way to satisfy the high demands in the clinical practice. However, the reported methods ignore the mutual dependencies of low-and high-resolution images and did not fully exploit the dependencies between channels. To tackle with the drawbacks, we propose a novel network for fundus image SR, named by Fundus Cascaded Channel-wise Attention Network (FC-CAN). The proposed FCCAN cascades channel attention module and dense module jointly to exploit the semantic interdependencies both frequency and domain information across channels. The channel attention module rescales channel maps in spatial domain, while the dense module preserves the HR components by up- and down-sampling operation. Experimental results demonstrate the superiority of our net-work in comparison with the six methods.