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RealFuVSR:Feature enhanced real-world video super-resolution

Virtual Reality & Intelligent Hardware 2023年第6期5卷 523-537页

作者： Zhi LI Xiongwen PANG Yiyue JIANG Yujie WANG School of Artificial Intelligence South China Normal UniversityGuangzhou 510631China School of Computer Science South China Normal UniversityGuangzhou 510631China School of Economics and Management South China Normal UniversityGuangzhou 510631China

Background Recurrent recovery is a common method for video super-resolution(VSR)that models the correlation between frames via hidden ***,the application of this structure in real-world scenarios can lead to unsatisfactory *** found that in real-world VSR training,the use of unknown and complex degradation can better simulate the degradation process in the real *** Based on this,we propose the RealFuVSR model,which simulates real-world degradation and mitigates artifacts caused by the ***,we propose a multiscale feature extraction module(MSF)module that extracts and fuses features from multiple scales,thereby facilitating the elimination of hidden state *** improve the accuracy of the hidden state alignment information,RealFuVSR uses an advanced optical flow-guided deformable ***,a cascaded residual upsampling module was used to eliminate noise caused by the upsampling *** The experiment demonstrates that RealFuVSR model can not only recover high-quality videos but also outperforms the state-of-the-art RealBasicVSR and RealESRGAN models.

关键词： video super-resolution Deformable convolution Cascade residual upsampling Second-order degradation Multi-scale feature extraction

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Cross-Dimensional video super-resolution Exploring Features Interactions and Dependencies

Cross-Dimensional Video Super-Resolution Exploring Features ...

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作者： Yacouba Coulibaly 中国石油大学(北京)

学位级别：硕士

video super-resolution（VSR）is a deep learning-based video restoration task that aims to improve video *** a series of low-resolution（LR）input frames,the goal of VSR is to map the corresponding central high-resolution（HR）frame by exploring the temporal connection between consecutive *** convolution neural network（CNN）,due to its high efficiency in computer vision tasks,has been widely used in ***,given the input frames,these methods try to align the reference frame with the supporting frames using optical *** motion estimation and motion compensation（MEMC）using optical flow is computationally expensive and fails to guarantee proper high resolution（HR）.This study presents a novel approach to video super-resolution（VSR）that addresses the challenges of computational complexity and time consumption in existing *** focuses on extracting shared information from the reference frame and its neighboring frames and how different features across spatial,temporal,and channel dimensions interact and depend on each other to improve video *** achieve this,the proposed approach first adopts a three-step learning pipeline:efficient fast learning with the fast-learning block（3D-EFL）.The 3D-EFL introduces 3D depthwise separable convolution to learn feature representation from input frames with few computational *** second step consists of a triplet attention learning strategy that investigates the interactions between spatial and channel dimensions and exploits these interactions to enhance video *** final step consists of increasing the spatial dimension of the feature and high-resolving the reference ***,it adopts low-residual and high-residual learning to connect these steps,alleviating the gradient vanishing problem and allowing more gradient *** proposed approach not only reduces computational complexity but also provides an accurate high-resolution（HR）output without the need for inter-frame mot

关键词： deep learning video super-resolution attention neural network cross-dimensional learning dimensional reduction

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Spatio-Temporal Adaptive super-resolution Reconstruction Model Based on Zemike Moment for Spatial video Sequences

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China Communications 2012年第12期9卷 93-107页

作者： Liang Meiyu Du Junping JangMyung Lee Liu Honggang Zhang Yun Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia School of Computer Science Beijing University of Posts and Telecommunications Beijing 100876 P. R. China Department of Electronics Engineering Pusan Nation University Busan Korea

video super-resolution （SR） reconstruction produces video sequences with High resolution （HR） via the fusion of several Low-resolution （LR） video frames. Traditional methods rely on the accurate estimation of subpixel motion, which constrains their applicability to video sequences with relatively simple motions such as global translation. We propose an efficient iterative spatio-temporal adaptive SR reconstruction model based on Zemike Moment （ZM）, which is effective for spatial video sequences with arbitrary motion. The model uses region correlation judgment and self-adaptive threshold strategies to improve the effect and time efficiency of the ZM-based SR method. This leads to better mining of non-local self-similarity and local structural regularity, and is robust to noise and rotation. An efficient iterative curvature-based interpolation scheme is introduced to obtain the initial HR estimation of each LR video frame. Experimental results both on spatial and standard video sequences demonstrate that the proposed method outperforms existing methods in terms of both subjective visual and objective quantitative evaluations, and greatly improves the time efficiency.

关键词： video super-resolution fuzzy registration scheme Zemike moment non-local self-similarity self-adaptive threshold

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