图像引导滤波的局部多尺度Retinex算法

Retinex算法是一种用于消除由光照变化给图像所带来的负面影响的图像增强算法。该算法的求解通常需要基于入射分量分段光滑的假设,利用正则化的方法迭代求解,计算效率低。文中基于一项最近提出的研究——"图像引导滤波",提出一种非迭代的Retinex算法框架。基于反射分量也满足分段光滑的假设,采用两次图像引导滤波克服了图像噪声所带来的影响。然后在基于小波变换域图像融合策略的基础上,提出基于图像引导滤波的多尺度Retinex算法,实现图像细节增强与颜色保真之间的平衡。实验结果表明,与各种算法相比,该算法在克服噪声、细节增强和颜色保真方面能够取得更好的效果。

Local multi-scale Retinex algorithm based on guided image filtering

Retinex algorithm deals with the removal of unfavorable illumination effects from a given image.Solving it is typically done by introducing a regularization that forces a spatial smoothness on the illumination, which is computational expensive.In this paper we propose a non-iterative retinex algorithm based on a recent"guided image filter".Assuming a spatial smoothness on the reflectance, a method using two guided image filters is applied to eliminate artifacts caused by noise.Then, a multi-resolution framework combining guided image filtering and wavelet thresholding,is presented.Our framework is very effective in achieving a trade-off between detail enhancement and color constancy.Compared to other enhancement algorithms,our results verify the new approach's efficiency in eliminating artifacts caused by noise,detail enhancement,and color constancy.