Detail-Enhanced Wavelet Residual Network for Single Image Super-Resolution

计算机科学 人工智能 小波 残余物 计算机视觉 加权 图像(数学) 迭代重建 图像分辨率 模式识别(心理学) 算法 声学 物理
作者
Wei‐Yen Hsu,Pei-Wen Jian
出处
期刊:IEEE Transactions on Instrumentation and Measurement [Institute of Electrical and Electronics Engineers]
卷期号:71: 1-13 被引量:25
标识
DOI:10.1109/tim.2022.3192280
摘要

Single-image super-resolution (SR) is vital in all areas of computer vision, due to the capability of the technology to generate high-resolution (HR) images. Conventional SR approaches do not consider high-frequency detail information during the reconstruction, resulting in high-frequency details of the image unreal, distorted in the reconstructed SR image. In this study, a novel detail-enhanced wavelet residual network (DeWRNet) is proposed to individually deal with the low- and high-frequency of sub-images and resolve the problem of the details over smooth with a novel low-to-high frequency transmission (L2HFT) and detail enhancement (DE) mechanism. Unlike traditional SR approaches, which directly predict high-resolution images, the proposed DeWRNet decomposes the image into low- and high-frequency ones through stationary wavelet transform, and trains low- and high-frequency sub-images with different models. Furthermore, while reconstructing high-frequency details, low-frequency structure is also provided to further restore and enhance high-frequency details by the proposed L2HFT and DE mechanism. Finally, the joint-loss function is used to optimize low- and high-frequency results in different degree of weighting. In addition to correct restoration, image details are further enhanced by adjusting different hyperparameters during training. Compared with the state-of-the-art approaches, the experimental results indicate that the proposed DeWRNet achieves a better performance and has excellent visual presentation, especially in image edges and texture details.
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