Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency

• URL: http://arxiv.org/abs/2111.04506v1
• Date: Fri, 5 Nov 2021 07:52:06 GMT
• Title: Self-Supervised Intrinsic Image Decomposition Network Considering Reflectance Consistency
• Authors: Yuma Kinoshita and Hitoshi Kiya
• Abstract summary: Intrinsic image decomposition aims to decompose an image into illumination-invariant and illumination-variant components. Our network can decompose images into reflectance and shading components.
• Score: 23.300763504208593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a novel intrinsic image decomposition network considering reflectance consistency. Intrinsic image decomposition aims to decompose an image into illumination-invariant and illumination-variant components, referred to as ``reflectance'' and ``shading,'' respectively. Although there are three consistencies that the reflectance and shading should satisfy, most conventional work does not sufficiently account for consistency with respect to reflectance, owing to the use of a white-illuminant decomposition model and the lack of training images capturing the same objects under various illumination-brightness and -color conditions. For this reason, the three consistencies are considered in the proposed network by using a color-illuminant model and training the network with losses calculated from images taken under various illumination conditions. In addition, the proposed network can be trained in a self-supervised manner because various illumination conditions can easily be simulated. Experimental results show that our network can decompose images into reflectance and shading components.

Related papers

• Colorful Diffuse Intrinsic Image Decomposition in the Wild [0.0]
  Intrinsic image decomposition aims to separate the surface reflectance and the effects from the illumination given a single photograph. In this work, we separate an input image into its diffuse albedo, colorful diffuse shading, and specular residual components. Our extended intrinsic model enables illumination-aware analysis of photographs and can be used for image editing applications.
  arXiv  Detail & Related papers  (2024-09-20T17:59:40Z)
• Diffusion Reflectance Map: Single-Image Stochastic Inverse Rendering of Illumination and Reflectance [19.20790327389337]
  Reflectance bounds the frequency spectrum of illumination in the object appearance. We introduce the first inverse rendering method, which recovers the attenuated frequency spectrum of an illumination jointly with the reflectance of an object of known geometry.
  arXiv  Detail & Related papers  (2023-12-07T18:50:00Z)
• DANI-Net: Uncalibrated Photometric Stereo by Differentiable Shadow Handling, Anisotropic Reflectance Modeling, and Neural Inverse Rendering [75.86523223933912]
  Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by the unknown light. We propose DANI-Net, an inverse rendering framework with differentiable shadow handling and anisotropic reflectance modeling. Our network benefits from cues of shadow and anisotropic reflectance through two differentiable paths.
  arXiv  Detail & Related papers  (2023-03-27T11:10:54Z)
• Nighttime Smartphone Reflective Flare Removal Using Optical Center Symmetry Prior [81.64647648269889]
  Reflective flare is a phenomenon that occurs when light reflects inside lenses, causing bright spots or a "ghosting effect" in photos. We propose an optical center symmetry prior, which suggests that the reflective flare and light source are always symmetrical around the lens's optical center. We create the first reflective flare removal dataset called BracketFlare, which contains diverse and realistic reflective flare patterns.
  arXiv  Detail & Related papers  (2023-03-27T09:44:40Z)
• Estimating Reflectance Layer from A Single Image: Integrating Reflectance Guidance and Shadow/Specular Aware Learning [66.36104525390316]
  We propose a two-stage learning method, including reflectance guidance and a Shadow/Specular-Aware (S-Aware) network to tackle the problem. In the first stage, an initial reflectance layer free from shadows and specularities is obtained with the constraint of novel losses. To further enforce the reflectance layer to be independent of shadows and specularities in the second-stage refinement, we introduce an S-Aware network that distinguishes the reflectance image from the input image.
  arXiv  Detail & Related papers  (2022-11-27T07:26:41Z)
• NeIF: Representing General Reflectance as Neural Intrinsics Fields for Uncalibrated Photometric Stereo [70.71400320657035]
  Uncalibrated photometric stereo (UPS) is challenging due to the inherent ambiguity brought by unknown light. This paper establishes an implicit relation between light clues and light estimation and solves UPS in an unsupervised manner.
  arXiv  Detail & Related papers  (2022-08-18T15:11:24Z)
• Invariant Descriptors for Intrinsic Reflectance Optimization [15.638996731038231]
  Intrinsic image decomposition aims to factorize an image into albedo (reflectance) and shading (illumination) sub-components. Being ill-posed and under-constrained, it is a very challenging computer vision problem. Our approach is physics-based, learning-free and leads to more accurate and robust reflectance decompositions.
  arXiv  Detail & Related papers  (2022-04-08T13:52:13Z)
• Low-Light Image Enhancement with Normalizing Flow [92.52290821418778]
  In this paper, we investigate to model this one-to-many relationship via a proposed normalizing flow model. An invertible network that takes the low-light images/features as the condition and learns to map the distribution of normally exposed images into a Gaussian distribution. The experimental results on the existing benchmark datasets show our method achieves better quantitative and qualitative results, obtaining better-exposed illumination, less noise and artifact, and richer colors.
  arXiv  Detail & Related papers  (2021-09-13T12:45:08Z)
• Intrinsic Image Transfer for Illumination Manipulation [1.2387676601792899]
  This paper presents a novel intrinsic image transfer (IIT) algorithm for illumination manipulation. It creates a local image translation between two illumination surfaces. We illustrate that all losses can be reduced without the necessity of taking an intrinsic image decomposition.
  arXiv  Detail & Related papers  (2021-07-01T19:12:24Z)
• A Switched View of Retinex: Deep Self-Regularized Low-Light Image Enhancement [5.217306793654357]
  Self-regularized low-light image enhancement does not require any normal-light image in training, thereby freeing from the chains on paired or unpaired low-/normal-images. This paper presents a novel self-regularized method based on Retinex, which preserves all colors (Hue, Saturation) and only integrates Retinex theory into brightness (Value) Our method is efficient as a low-light image is decoupled into two subspaces, color and brightness, for better preservation and enhancement.
  arXiv  Detail & Related papers  (2021-01-03T10:40:31Z)
• Polarized Reflection Removal with Perfect Alignment in the Wild [66.48211204364142]
  We present a novel formulation to removing reflection from polarized images in the wild. We first identify the misalignment issues of existing reflection removal datasets. We build a new dataset with more than 100 types of glass in which obtained transmission images are perfectly aligned with input mixed images.
  arXiv  Detail & Related papers  (2020-03-28T13:29:31Z)

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.