Variational models for joint subsampling and reconstruction of turbulence-degraded images

12/08/2017
by   Chun Pong Lau, et al.
0

Turbulence-degraded image frames are distorted by both turbulent deformations and space-time-varying blurs. To suppress these effects, we propose a multi-frame reconstruction scheme to recover a latent image from the observed image sequence. Recent approaches are commonly based on registering each frame to a reference image, by which geometric turbulent deformations can be estimated and a sharp image can be restored. A major challenge is that a fine reference image is usually unavailable, as every turbulence-degraded frame is distorted. A high-quality reference image is crucial for the accurate estimation of geometric deformations and fusion of frames. Besides, it is unlikely that all frames from the image sequence are useful, and thus frame selection is necessary and highly beneficial. In this work, we propose a variational model for joint subsampling of frames and extraction of a clear image. A fine image and a suitable choice of subsample are simultaneously obtained by iteratively reducing an energy functional. The energy consists of a fidelity term measuring the discrepancy between the extracted image and the subsampled frames, as well as regularization terms on the extracted image and the subsample. Different choices of fidelity and regularization terms are explored. By carefully selecting suitable frames and extracting the image, the quality of the reconstructed image can be significantly improved. Extensive experiments have been carried out, which demonstrate the efficacy of our proposed model. In addition, the extracted subsamples and images can be put in existing algorithms to produce improved results.

READ FULL TEXT

page 18

page 21

page 22

page 23

page 25

page 26

page 29

page 34

research
04/11/2017

Restoration of Atmospheric Turbulence-distorted Images via RPCA and Quasiconformal Maps

We address the problem of restoring a high-quality image from an observe...
research
01/17/2014

Distortion-driven Turbulence Effect Removal using Variational Model

It remains a challenge to simultaneously remove geometric distortion and...
research
03/06/2023

Butterfly: Multiple Reference Frames Feature Propagation Mechanism for Neural Video Compression

Using more reference frames can significantly improve the compression ef...
research
05/31/2021

Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information

Many deep learning based video compression artifact removal algorithms h...
research
12/01/2021

Learning Transformer Features for Image Quality Assessment

Objective image quality evaluation is a challenging task, which aims to ...
research
04/08/2021

Affine-modeled video extraction from a single motion blurred image

A motion-blurred image is the temporal average of multiple sharp frames ...
research
07/05/2017

On the Fusion of Compton Scatter and Attenuation Data for Limited-view X-ray Tomographic Applications

In this paper we demonstrate the utility of fusing energy-resolved obser...

Please sign up or login with your details

Forgot password? Click here to reset