Single-image super-resolution via a lightweight convolutional neural network with improved shuffle learning

Lu, Xinbiao; Xie, Xupeng; Ye, Chunlin; Xing, Hao; Liu, Zecheng; Chen, Yudan

doi:10.1007/s11760-023-02730-9

Cited by 4 publications

(1 citation statement)

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“…Super-resolution (SR) is an important visual task that aims to recover high-resolution (HR) images from low-resolution (LR) observations. Single-image super-resolution (SISR) [1][2][3][4][5][6][7][8][9] methods synthesize high-frequency content by responding to image priors from large datasets or self-similarity within images. In contrast to single-image super-resolution reconstruction, video super-resolution reconstruction (VSR) can exploit the correlation between spatio-temporal information of consecutive frames, and the goal of VSR is to reconstruct high-resolution (HR) frames from their corresponding low-resolution (LR) frames with the assistance of neighboring LR frames.…”

Section: Introductionmentioning

confidence: 99%

Dense video super-resolution time-differential network with feature enrichment module

Wu,

Ma,

Chen

2023

Preprint

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Video super-resolution is capable of recovering high-resolution images from multiple low-resolution images, where loop structures are a common frame choice for video super-resolution tasks. BasicVSR employs bidirectional propagation and feature alignment to efficiently utilize information from the entire input video. In this work, we improved the performance of the network by revisiting the role of the various modules in BasicVSR and redesigning the network. Firstly, we will maintain centralized communication with the reference frame through the reference-based feature enrichment module after optical flow distortion, which is helpful for handling complex motion, and at the same time, for the selected keyframe, according to the degree of motion deviation of the adjacent frame relative to the keyframe, it is divided into two different regions, and the model with different receptive fields is adopted for feature extraction to further alleviate the accumulation of alignment errors. In the feature correction module, we modify the simple residual block stack to RIR structure, and fuse different levels of features with each other, which can make the final feature information more comprehensive and abundant. In addition, dense connection are introduced in the reconstruction module to promote the full use of hierarchical feature information for better reconstruction. Experimental verification is carried out on two public datasets: Vid4 and REDS4, and the comparative results show that compared with BasicVSR, the PSNR quantitative indexes of the proposed improved model on the two datasets are improved by 0.20dB and 0.33dB, respectively. In addition, from the point of view of visual perception, the model can effectively improve the clarity of the image and reduce artifacts.

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