残余物
人工智能
计算机科学
特征提取
模式识别(心理学)
块(置换群论)
特征(语言学)
迭代重建
图像分辨率
计算机视觉
作者
Yetong Wang,Kongduo Xing,Baji Wang,Sheng Hai,Jiayao Li,MingXin Deng
标识
DOI:10.1117/1.jei.31.3.033010
摘要
With the development of artificial intelligence, deep learning has been widely used in image super-resolution reconstruction. To solve the problems of feature extraction insufficiency, detail loss, and gradient disappearance in super-resolution reconstruction based on traditional deep learning, we propose a lightweight multihierarchical feature fusion network for single-image super-resolution. An important part of our network is dual residual block. To better extract features and reduce the amount of parameters as much as possible, the dual residual block we designed is an excite-and-squeeze structure. To transmit feature information, webadd autocorrelation weight unit into dual-residual block, which can weight each channel according to the image feature information. Extensive experiments show that our method is significantly better than LapSRN, MSRN, and other representative methods. The PSNR on SET14, URBAN100, and MANGA109 datasets are improved by 5 dB and SSIM is improved by 4% compared with the baseline method.
科研通智能强力驱动
Strongly Powered by AbleSci AI