Double-Layer Search and Adaptive Pooling Fusion for Reference-Based Image Super-Resolution

Abstract

Reference-based image super-resolution (RefSR) aims to reconstruct high-resolution (HR) images from low-resolution (LR) images by introducing HR reference images. The key step of RefSR is to transfer reference features to LR features. However, existing methods still lack an efficient transfer mechanism, resulting in blurry details in the generated image. In this article, we propose a double-layer search module and an adaptive pooling fusion module group for reference-based image super-resolution, called DLASR. Based on the re-search strategy, the double-layer search module can produce an accurate index map and score map. These two maps are used to filter out accurate reference features, which greatly increases the efficiency of feature transfer in the later stage. Through two continuous feature-enhancement steps, the adaptive pooling fusion module group can transfer more valuable reference features to the corresponding LR features. In addition, a structure reconstruction module is proposed to recover the geometric information of the images, which further improves the visual quality of the generated image. We conduct comparative experiments on a variety of datasets, and the results prove that DLASR achieves significant improvements over other state-of-the-art methods, in terms of quantitative accuracy and qualitative visual effect. The code is available at https://github.com/clttyou/DLASR.