Dense Scattering Layer Removal
This work addresses visibility restoration in challenging environments such as underwater or foggy scenes, which is an incremental improvement in image processing for applications like photography or surveillance.
The paper tackles the problem of removing dense scattering layers like fog or underwater haze from single images, which often magnifies impurities, and introduces a transmission-aware optimization method with non-local regularization to reduce artifacts and improve visibility.
We propose a new model, together with advanced optimization, to separate a thick scattering media layer from a single natural image. It is able to handle challenging underwater scenes and images taken in fog and sandstorm, both of which are with significantly reduced visibility. Our method addresses the critical issue -- this is, originally unnoticeable impurities will be greatly magnified after removing the scattering media layer -- with transmission-aware optimization. We introduce non-local structure-aware regularization to properly constrain transmission estimation without introducing the halo artifacts. A selective-neighbor criterion is presented to convert the unconventional constrained optimization problem to an unconstrained one where the latter can be efficiently solved.