Single image super-resolution (SR) is an important topic in computer vision because of its ability to gen-erate high-resolution (HR) images. Traditional SR methods do not pay attention to high-frequency detail perception in the reconstruction process, resulting in unrealistic high-frequency details of images. To ad-dress the problem of over-smoothing of details, a novel wavelet detail perception network (WDPNet) is proposed in this study. Different from traditional SR methods that directly restore high-resolution im-ages, the proposed WDPNet decomposes images into low-frequency and high-frequency sub-images by wavelet transform and then uses different models to train these sub-images. Moreover, low-frequency structures are also provided to the high-frequency model to further recover and enhance high-frequency details through the proposed low-to-high information delivery (L2HID) and detail perception enhance-ment (DPE) mechanisms. Finally, the low-frequency and high-frequency models are fused and weighted to different degrees to enhance image details further. Compared with the state-of-the-art methods, the experimental results show that the proposed WDPNet achieves better performance and visual results in image detail perception.(c) 2022 Elsevier B.V. All rights reserved.
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