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Multi-Frame Super-Resolution

6 papers with code · Computer Vision

When multiple images of the same view are taken from slightly different positions, perhaps also at different times, then they collectively contain more information than any single image on its own. Multi-Frame Super-Resolution fuses these low-res inputs into a composite high-res image that can reveal some of the original detail that cannot be recovered from any low-res image alone.

( Credit: HighRes-net )

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Greatest papers with code

Wide Activation for Efficient and Accurate Image Super-Resolution

27 Aug 2018krasserm/super-resolution

Keras-based implementation of WDSR, EDSR and SRGAN for single image super-resolution

MULTI-FRAME SUPER-RESOLUTION

HighRes-net: Recursive Fusion for Multi-Frame Super-Resolution of Satellite Imagery

15 Feb 2020ElementAI/HighRes-net

Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.

DE-ALIASING IMAGE REGISTRATION MULTI-FRAME SUPER-RESOLUTION

HighRes-net: Multi-Frame Super-Resolution by Recursive Fusion

ICLR 2020 ElementAI/HighRes-net

Multi-frame Super-Resolution (MFSR) offers a more grounded approach to the ill-posed problem, by conditioning on multiple low-resolution views.

DE-ALIASING IMAGE REGISTRATION MULTI-FRAME SUPER-RESOLUTION

Frame-Recurrent Video Super-Resolution

CVPR 2018 msmsajjadi/frvsr

Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images.

MOTION COMPENSATION MULTI-FRAME SUPER-RESOLUTION VIDEO SUPER-RESOLUTION

Handheld Multi-Frame Super-Resolution

8 May 2019JVision/Handheld-Multi-Frame-Super-Resolution

In this paper, we supplant the use of traditional demosaicing in single-frame and burst photography pipelines with a multiframe super-resolution algorithm that creates a complete RGB image directly from a burst of CFA raw images.

DEMOSAICKING MULTI-FRAME SUPER-RESOLUTION

DeepSUM: Deep neural network for Super-resolution of Unregistered Multitemporal images

15 Jul 2019diegovalsesia/deepsum

This novel framework integrates the spatial registration task directly inside the CNN, and allows to exploit the representation learning capabilities of the network to enhance registration accuracy.

MULTI-FRAME SUPER-RESOLUTION REPRESENTATION LEARNING