Dense scattering layer removal

Qiong Yan, Li Xu, Jiaya Jia
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引用次数: 21

Abstract

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.
密集散射层去除
我们提出了一种新的模型,结合先进的优化,从单个自然图像中分离出厚散射介质层。它能够处理具有挑战性的水下场景和在雾和沙尘暴中拍摄的图像,这两种情况的能见度都大大降低。我们的方法通过传输感知优化解决了关键问题,即在去除散射介质层后,原本不明显的杂质将被大大放大。我们引入非局部结构感知正则化来适当约束传输估计,而不引入光晕伪影。提出了一种选择邻居准则,将非常规约束优化问题转化为可有效求解的无约束优化问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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