Multi-color balance for color constancy

• URL: http://arxiv.org/abs/2105.10228v1
• Date: Fri, 21 May 2021 09:38:56 GMT
• Title: Multi-color balance for color constancy
• Authors: Teruaki Akazawa, Yuma Kinoshita and Hitoshi Kiya
• Abstract summary: The proposed method, called "n-color balancing," allows us not only to perfectly correct n target colors on the basis of corresponding ground truth colors but also to correct colors other than the n colors. Although white-balancing can perfectly adjust white, colors other than white are not considered in the framework of white-balancing in general.
• Score: 19.723551683930772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel multi-color balance adjustment for color constancy. The proposed method, called "n-color balancing," allows us not only to perfectly correct n target colors on the basis of corresponding ground truth colors but also to correct colors other than the n colors. In contrast, although white-balancing can perfectly adjust white, colors other than white are not considered in the framework of white-balancing in general. In an experiment, the proposed multi-color balancing is demonstrated to outperform both conventional white and multi-color balance adjustments including Bradford's model.

Related papers

• Colorizing Monochromatic Radiance Fields [55.695149357101755]
  We consider reproducing color from monochromatic radiance fields as a representation-prediction task in the Lab color space. By first constructing the luminance and density representation using monochromatic images, our prediction stage can recreate color representation on the basis of an image colorization module. We then reproduce a colorful implicit model through the representation of luminance, density, and color.
  arXiv  Detail & Related papers  (2024-02-19T14:47:23Z)
• Control Color: Multimodal Diffusion-based Interactive Image Colorization [81.68817300796644]
  Control Color (Ctrl Color) is a multi-modal colorization method that leverages the pre-trained Stable Diffusion (SD) model. We present an effective way to encode user strokes to enable precise local color manipulation. We also introduce a novel module based on self-attention and a content-guided deformable autoencoder to address the long-standing issues of color overflow and inaccurate coloring.
  arXiv  Detail & Related papers  (2024-02-16T17:51:13Z)
• Whitening-based Contrastive Learning of Sentence Embeddings [61.38955786965527]
  This paper presents a whitening-based contrastive learning method for sentence embedding learning (WhitenedCSE) We find that these two approaches are not totally redundant but actually have some complementarity due to different uniformity mechanism.
  arXiv  Detail & Related papers  (2023-05-28T14:58:10Z)
• Auto White-Balance Correction for Mixed-Illuminant Scenes [52.641704254001844]
  Auto white balance (AWB) is applied by camera hardware to remove color cast caused by scene illumination. This paper presents an effective AWB method to deal with such mixed-illuminant scenes. Our method does not require illuminant estimation, as is the case in traditional camera AWB modules.
  arXiv  Detail & Related papers  (2021-09-17T20:13:31Z)
• Spatially varying white balancing for mixed and non-uniform illuminants [19.723551683930772]
  We propose a novel white balance adjustment, called "spatially varying white balancing," for single, mixed, and non-uniform illuminants. In an experiment, the effectiveness of the proposed method is shown under mixed and non-uniform illuminants, compared with conventional white and multi-color balancing.
  arXiv  Detail & Related papers  (2021-09-03T07:26:11Z)
• Guided Colorization Using Mono-Color Image Pairs [6.729108277517129]
  monochrome images usually have better signal-to-noise ratio (SNR) and richer textures due to its higher quantum efficiency. We propose a mono-color image enhancement algorithm that colorizes the monochrome image with the color one. Experimental results show that, our algorithm can efficiently restore color images with higher SNR and richer details from the mono-color image pairs.
  arXiv  Detail & Related papers  (2021-08-17T07:00:28Z)
• Image color correction, enhancement, and editing [14.453616946103132]
  We study the color correction problem from the standpoint of the camera's image signal processor (ISP) In particular, we propose auto image recapture methods to generate different realistic versions of the same camera-rendered image with new colors.
  arXiv  Detail & Related papers  (2021-07-28T01:14:12Z)
• CoRe: Color Regression for Multicolor Fashion Garments [80.57724826629176]
  In this paper, we handle color detection as a regression problem to predict the exact RGB values. We include a second regression stage for refinement in our newly proposed architecture. This architecture is modular and easily expanded to detect the RGBs of all colors in a multicolor garment.
  arXiv  Detail & Related papers  (2020-10-06T16:12:30Z)
• Deep White-Balance Editing [50.08927449718674]
  Cameras capture sensor images that are rendered by their integrated signal processor (ISP) to a standard RGB (sRGB) color space encoding. Recent work by [3] showed that sRGB images that were rendered with the incorrect white balance cannot be easily corrected due to the ISP's nonlinear rendering. We propose to solve this problem with a deep neural network (DNN) architecture trained in an end-to-end manner to learn the correct white balance.
  arXiv  Detail & Related papers  (2020-04-03T03:18:42Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.