Non-Rigid Distortion Removal via Coordinate Based Image Representation

maging through turbulent refractive medium (e.g., hot air, in-homogeneous gas, fluid flow) is challenging, since the non-linear light transport through the medium (e.g. refraction and scattering) causes non-rigid distortions in perceived images. However, most computer vision algorithms rely on sharp and distortion-free images to achieve the expected performance. Removal of these non-rigid image distortions is therefore critical and beneficial for many vision applications, from segmentation to recognition. To resolve the distortion and blur introduced by air turbulence, conventional turbulence restoration methods leverage optical flow, regions fusion and blind deconvolution to recover images. One avenue that is underexplored for this problem is the use of coordinate based image representations. These methods represent images as the parameters of a neural network ,and they can be used to deform the image grid itself to account for turbulence. In this research, we aim to extend this idea to unseen images with meta learning that can remove both air and water distortions without much customization.

Related publications:

  1. Unsupervised Non-Rigid Image Distortion Removal via Grid Deformation, ICCV 2021