This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework consisting of three photo-realistic losses defined on the sub-layers factorized by an intrinsic image decomposition. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. Moreover, with a series of relaxations, all of them can be directly defined on images, giving a closed-form solution for image illumination manipulation. This new paradigm differs from the prevailing Retinex-based algorithms, as it provides an implicit way to deal with the per-pixel image illumination. We finally demonstrate its versatility and benefits to the illumination-related tasks such as illumination compensation, image enhancement, and high dynamic range (HDR) image compression, and show the high-quality results on natural image datasets.

翻译：本文展示了一种新型的内在图像转换( IIT) 操作光化的算法, 它在两个光化表面之间创建了本地图像翻译。 这个模型建于一个基于优化的框架上, 包括由内在图像分解的分解因素在子层中定义的三种光- 现实损失。 我们演示了所有损失都可以减少, 而不必在众所周知的空间- 差异化染色( 光化) 光化- 变化反射之前的知识下进行内在图像分解 。 此外, 通过一系列的放松, 它们都可以在图像上直接定义, 给图像照明操作提供一个封闭式的解决方案 。 这个新模式与流行的 Retinex 算法不同, 因为它提供了处理半像素图像分解的隐含方法 。 我们最终展示了其多功能和对与照明有关的任务的好处, 如照明补偿、 图像增强和高动态范围图像压缩, 并展示自然图像集的高质量结果 。