HistoGAN: Controlling Colors of GAN-Generated and Real Images via Color Histograms
While generative adversarial networks (GANs) can successfully produce high-quality images, they can be challenging to control. Simplifying GAN-based image generation is critical for their adoption in graphic design and artistic work. This goal has led to significant interest in methods that can intuitively control the appearance of images generated by GANs. In this paper, we present HistoGAN, a color histogram-based method for controlling GAN-generated images' colors. We focus on color histograms as they provide an intuitive way to describe image color while remaining decoupled from domain-specific semantics. Specifically, we introduce an effective modification of the recent StyleGAN architecture to control the colors of GAN-generated images specified by a target color histogram feature. We then describe how to expand HistoGAN to recolor real images. For image recoloring, we jointly train an encoder network along with HistoGAN. The recoloring model, ReHistoGAN, is an unsupervised approach trained to encourage the network to keep the original image's content while changing the colors based on the given target histogram. We show that this histogram-based approach offers a better way to control GAN-generated and real images' colors while producing more compelling results compared to existing alternative strategies.
Code (1)
Tasks
Image GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Selective Synthetic Augmentation with HistoGAN for Improved Histopathology Image Classification
Histopathological analysis is the present gold standard for precancerous lesion diagnosis. The goal of automated histopathological classification from digital images requires supervised training, which requires a large n…
Classificationimage-classificationImage Classificationwhole slide imagesDistinguishing Natural and Computer-Generated Images using Multi-Colorspace fused EfficientNet
The problem of distinguishing natural images from photo-realistic computer-generated ones either addresses natural images versus computer graphics or natural images versus GAN images, at a time. But in a real-world image…
Decision MakingImage ForensicsImage GenerationTransfer LearningControlling the color appearance of objects by optimizing the illumination spectrum
We have developed an innovative lighting system that changes specific target colors while keeping the lights appearing naturally white. By precisely controlling the spectral power distribution (SPD) of illumination and h…
MetamerismAuto-painter: Cartoon Image Generation from Sketch by Using Conditional Generative Adversarial Networks
Recently, realistic image generation using deep neural networks has become a hot topic in machine learning and computer vision. Images can be generated at the pixel level by learning from a large collection of images. Le…
Image Generationsemantic image synthesis of anime characters based on conditional generative adversarial networks
The goal of semantic image synthesis is to generate realistic images from semantic label maps. However, current approaches for generating anime characters from semantic label maps still encounter some issues, particularl…
Edge DetectionGenerative Adversarial NetworkImage GenerationSemantic Segmentation