Probabilistic Color Constancy

• URL: http://arxiv.org/abs/2005.02730v1
• Date: Wed, 6 May 2020 11:03:05 GMT
• Title: Probabilistic Color Constancy
• Authors: Firas Laakom, Jenni Raitoharju, Alexandros Iosifidis, Uygar Tuna, Jarno Nikkanen and Moncef Gabbouj
• Abstract summary: We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.
• Score: 88.85103410035929
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel unsupervised color constancy method, called Probabilistic Color Constancy (PCC). We define a framework for estimating the illumination of a scene by weighting the contribution of different image regions using a graph-based representation of the image. To estimate the weight of each (super-)pixel, we rely on two assumptions: (Super-)pixels with similar colors contribute similarly and darker (super-)pixels contribute less. The resulting system has one global optimum solution. The proposed method achieves competitive performance, compared to the state-of-the-art, on INTEL-TAU dataset.

Related papers

• Transforming Color: A Novel Image Colorization Method [8.041659727964305]
  This paper introduces a novel method for image colorization that utilizes a color transformer and generative adversarial networks (GANs) The proposed method integrates a transformer architecture to capture global information and a GAN framework to improve visual quality. Experimental results show that the proposed network significantly outperforms other state-of-the-art colorization techniques.
  arXiv  Detail & Related papers  (2024-10-07T07:23:42Z)
• Beyond Learned Metadata-based Raw Image Reconstruction [86.1667769209103]
  Raw images have distinct advantages over sRGB images, e.g., linearity and fine-grained quantization levels. They are not widely adopted by general users due to their substantial storage requirements. We propose a novel framework that learns a compact representation in the latent space, serving as metadata.
  arXiv  Detail & Related papers  (2023-06-21T06:59:07Z)
• Probabilistic Deep Metric Learning for Hyperspectral Image Classification [91.5747859691553]
  This paper proposes a probabilistic deep metric learning framework for hyperspectral image classification. It aims to predict the category of each pixel for an image captured by hyperspectral sensors. Our framework can be readily applied to existing hyperspectral image classification methods.
  arXiv  Detail & Related papers  (2022-11-15T17:57:12Z)
• PalGAN: Image Colorization with Palette Generative Adversarial Networks [51.59276436217957]
  We propose a new GAN-based colorization approach PalGAN, integrated with palette estimation and chromatic attention. PalGAN outperforms state-of-the-arts in quantitative evaluation and visual comparison, delivering notable diverse, contrastive, and edge-preserving appearances.
  arXiv  Detail & Related papers  (2022-10-20T12:28:31Z)
• Cross-Camera Deep Colorization [10.254243409261898]
  We propose an end-to-end convolutional neural network to align and fuse images from a color-plus-mono dual-camera system. Our method consistently achieves substantial improvements, i.e., around 10dB PSNR gain.
  arXiv  Detail & Related papers  (2022-08-26T11:02:14Z)
• Mitigating Channel-wise Noise for Single Image Super Resolution [33.383282898248076]
  We propose to super-resolve noisy color images by considering the color channels jointly. Results demonstrate the super-resolving capability of the approach in real scenarios.
  arXiv  Detail & Related papers  (2021-12-14T17:45:15Z)
• 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)
• ITSELF: Iterative Saliency Estimation fLexible Framework [68.8204255655161]
  Saliency object detection estimates the objects that most stand out in an image. We propose a superpixel-based ITerative Saliency Estimation fLexible Framework (ITSELF) that allows any user-defined assumptions to be added to the model. We compare ITSELF to two state-of-the-art saliency estimators on five metrics and six datasets.
  arXiv  Detail & Related papers  (2020-06-30T16:51:31Z)
• Steering Self-Supervised Feature Learning Beyond Local Pixel Statistics [60.92229707497999]
  We introduce a novel principle for self-supervised feature learning based on the discrimination of specific transformations of an image. We demonstrate experimentally that learning to discriminate transformations such as LCI, image warping and rotations, yields features with state of the art generalization capabilities.
  arXiv  Detail & Related papers  (2020-04-05T22:09:08Z)

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.