论文标题
DeepHist：图像到图像翻译的可区分关节和颜色直方图层
DeepHist: Differentiable Joint and Color Histogram Layers for Image-to-Image Translation
论文作者
论文摘要
我们提出了深层历史 - 一个新颖的深度学习框架，用于通过直方图增强网络，并通过解决图像到图像翻译问题来证明其强度。具体而言，给定输入图像和参考颜色分布，我们旨在生成带有输入（源）结构外观（内容）的输出图像，但具有参考的颜色。关键思想是一种新技术，用于可区分输出图像的关节和颜色直方图。我们进一步定义了根据地球推动者在输出和参考的颜色直方图之间的距离以及基于源和输出图像的关节直方图的相互信息损失之间的颜色分布损失。为了控制输出图像的颜色分布，显示了色彩传递，图像着色和边缘$ \ rightarrow $ photo的任务的有希望的结果。显示了与pix2pix和cyclygans的比较。
We present the DeepHist - a novel Deep Learning framework for augmenting a network by histogram layers and demonstrate its strength by addressing image-to-image translation problems. Specifically, given an input image and a reference color distribution we aim to generate an output image with the structural appearance (content) of the input (source) yet with the colors of the reference. The key idea is a new technique for a differentiable construction of joint and color histograms of the output images. We further define a color distribution loss based on the Earth Mover's Distance between the output's and the reference's color histograms and a Mutual Information loss based on the joint histograms of the source and the output images. Promising results are shown for the tasks of color transfer, image colorization and edges $\rightarrow$ photo, where the color distribution of the output image is controlled. Comparison to Pix2Pix and CyclyGANs are shown.
