A study of some super resolution techniques in video sequence

2012 ◽  
Author(s):  
Christophe Riedinger ◽  
Mohamed Nadhir Khemakhem ◽  
Gerard Chollet
Keyword(s):  
Video Sequence ◽  
Super Resolution

scholarly journals FISR: Deep Joint Frame Interpolation and Super-Resolution with a Multi-Scale Temporal Loss

2020 ◽  
Vol 34 (07) ◽  
pp. 11278-11286 ◽  
Author(s):  
Soo Ye Kim ◽  
Jihyong Oh ◽  
Munchurl Kim
Keyword(s):  
Video Sequence ◽  
Super Resolution ◽  
Motion Artifacts ◽  
Video Frame ◽  
High Definition ◽  
Frame Interpolation ◽  
Display Devices ◽  
Multi Scale ◽  
Training Scheme ◽  
Spatio Temporal

Super-resolution (SR) has been widely used to convert low-resolution legacy videos to high-resolution (HR) ones, to suit the increasing resolution of displays (e.g. UHD TVs). However, it becomes easier for humans to notice motion artifacts (e.g. motion judder) in HR videos being rendered on larger-sized display devices. Thus, broadcasting standards support higher frame rates for UHD (Ultra High Definition) videos (4K@60 fps, 8K@120 fps), meaning that applying SR only is insufficient to produce genuine high quality videos. Hence, to up-convert legacy videos for realistic applications, not only SR but also video frame interpolation (VFI) is necessitated. In this paper, we first propose a joint VFI-SR framework for up-scaling the spatio-temporal resolution of videos from 2K 30 fps to 4K 60 fps. For this, we propose a novel training scheme with a multi-scale temporal loss that imposes temporal regularization on the input video sequence, which can be applied to any general video-related task. The proposed structure is analyzed in depth with extensive experiments.

scholarly journals Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)

2021 ◽  
Vol 6 (1) ◽  
pp. 1-6
Author(s):  
Ayush Singh ◽  
Mehran Ebrahimi
Keyword(s):  
Video Sequence ◽  
Structural Information ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Single Image ◽  
Resolution Method ◽  
Edge Information ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution

Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented.

Super-resolution image reconstruction with edge adaptive weight in video sequence

2012 ◽  
Author(s):  
Ji Yong Kwon ◽  
Du Sic Yoo ◽  
Jong Hyun Park ◽  
Se Hyeok Park ◽  
Moon Gi Kang
Keyword(s):  
Image Reconstruction ◽  
Video Sequence ◽  
Super Resolution ◽  
Resolution Image ◽  
Adaptive Weight
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