DeepHist: Differentiable Joint and Color Histogram Layers for
Image-to-Image Translation
- URL: http://arxiv.org/abs/2005.03995v1
- Date: Wed, 6 May 2020 20:07:58 GMT
- Title: DeepHist: Differentiable Joint and Color Histogram Layers for
Image-to-Image Translation
- Authors: Mor Avi-Aharon, Assaf Arbelle, and Tammy Riklin Raviv
- Abstract summary: We present the DeepHist - a novel Deep Learning framework for augmenting a network by histogram layers.
We aim to generate an output image with the structural appearance (content) of the input (source) yet with the colors of the reference.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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