Attention mechanism feedback network for image super-resolution

Abstract

The number of photosensitive elements in the image sensor determines the resolution of the original image. One promising method for increasing the resolution of an image without increasing hardware cost is image super-resolution. Many existing image super-resolution methods based on deep learning lack feedback mechanisms and deal with feature channels equally, which hinders the expressive ability of deep learning networks and makes it difficult to resolve the dependence between high- and low-resolution (LR) images. An image super-resolution method based on an attention mechanism feedback network was presented in this study. First, the proposed multiscale separable convolution layer is used to extract the characteristics of the input image. Second, a feedback module is designed to process the feedback connections between subnetworks and generate richer high-level representations through densely connected upper and lower projection units, enabling the recovery of high-resolution (HR) images from LR images. Subsequently, an attention mechanism is introduced in the feedback module to redistribute channel attention resources, enhance interdependence between channels, and focus the network on learning high-frequency information. Finally, the reconstructed image was gradually generated using the reconstruction layer through an iterative network. The experimental results for ×2 to ×8 image super-resolution on five standard datasets showed that the quality of the reconstructed image of the proposed method outperforms that of the comparative image super-resolution methods in terms of subjective perception and objective evaluation indices. It can retain more detail for super-resolution and obtain better HR images.