中圖分類號： TN919.8,；TP183 文獻標識碼： A DOI：10.16157/j.issn.0258-7998.200051 中文引用格式： 劉佳,，安鶴男，李蔚,，等. 基于幀循環(huán)網(wǎng)絡的視頻超分辨率技術[J].電子技術應用,，2020，46(9)：43-46. 英文引用格式： Liu Jia,，An Henan,，Li Wei，et al. Video super-resolution based on frame recurrent network[J]. Application of Electronic Technique,，2020,，46(9)：43-46.
Video super-resolution based on frame recurrent network
Liu Jia，An Henan,，Li Wei,，Zhang Changlin，Tu Zhiwei
College of Electronics and Information Engineering,，Shenzhen University,，Shenzhen 518061，China
Abstract： Compared with single image super-resolution, video super-resolution needs to align and fuse time series images. This frame-recurrent-based video super-resolution network consists of three parts：(1)The frame sequence alignment network extracts the image features and aligns the neighbor frames to the center frame,；(2)The frame fusion network fuses the aligned frames and supplements the center frame information with the neighbor frame information,；(3)The super-resolution network enlarges the fused image to obtain the final high-definition image. Experiments show that, compared with existing algorithms, video super-resolution technology based on frame loop network produces sharper images and higher quality.
相比于單幅圖像超分辨,，視頻超分辨可分為對齊、融合,、重建3個步驟,。對齊網(wǎng)絡的結果會直接影響融合網(wǎng)絡與重建網(wǎng)絡的效果。早期,，基于深度學習的視頻超分辨方法[4]參考相鄰視頻幀之間的光流場扭曲鄰居幀從而達到對齊的目的,。然而，Xue Tianfan等人[5]指出基于光流場的對齊方法并非視頻超分辨的最優(yōu)解,，提出基于任務流的視頻超分辨率方法,；JO Y H等人[6]提出了隱式運動補償?shù)姆椒ㄒ?guī)避流場的計算。
