We propose a new procedure for image illumination correction with data-driven parameter choice. This procedure aims at estimating the reflectance image from a corrupted version in which the corruption is due to pointwise multiplicative illumination artifact. The $\log \text{-illumination}$ artefact consists of “smooth” variations of the intensity which are modelled by a function lying in a finite dimensional space. Then a $\gamma$ -correction is incorporated. The question of model selection is difficult to solve. We propose an entropy minimization criterion for the selection of both the approximating $\log \text{-illumination}$ space dimension and the $\gamma$ -coefficient, so that no parameter tuning is needed. Several experiments are presented using this approach. A comparison to other methods illustrates the relevance of this approach.

中文翻译：

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数字图像照明伪影校正的数据驱动参数选择

我们提出了一种新的程序，用于通过数据驱动参数选择进行图像照度校正。该过程旨在从损坏的版本估计反射率图像，其中损坏是由于逐点乘法照明伪像所致。的$ \ log \ text {-illumination} $伪影由强度的“平滑”变化组成，这些变化通过位于有限维空间中的函数来建模。然后一个$ \ gamma $ -更正被合并。选型的问题很难解决。我们提出了一个熵最小化准则来选择两个近似$ \ log \ text {-illumination} $ 空间尺寸和 $ \ gamma $ -系数，因此不需要参数调整。使用这种方法提出了一些实验。与其他方法的比较说明了此方法的相关性。