Depth-Aided Color Image Inpainting in Quaternion Domain

• URL: http://arxiv.org/abs/2503.16818v1
• Date: Fri, 21 Mar 2025 03:18:41 GMT
• Title: Depth-Aided Color Image Inpainting in Quaternion Domain
• Authors: Shunki Tatsumi, Ryo Hayakawa, Youji Iiguni,
• Abstract summary: We propose a depth-aided color image inpainting method in the quaternion domain, called depth-aided low-rank quaternion matrix completion (D-LRQMC)<n>In conventional inpainting techniques, the color image is expressed as a quaternion matrix by using the three imaginary parts as the color channels, whereas the real part is set to zero and has no information.<n>Our approach incorporates depth information as the real part of the quaternion representations, leveraging the correlation between color and depth to improve the result of inpainting.
• Score: 0.8739101659113155
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a depth-aided color image inpainting method in the quaternion domain, called depth-aided low-rank quaternion matrix completion (D-LRQMC). In conventional quaternion-based inpainting techniques, the color image is expressed as a quaternion matrix by using the three imaginary parts as the color channels, whereas the real part is set to zero and has no information. Our approach incorporates depth information as the real part of the quaternion representations, leveraging the correlation between color and depth to improve the result of inpainting. In the proposed method, we first restore the observed image with the conventional LRQMC and estimate the depth of the restored result. We then incorporate the estimated depth into the real part of the observed image and perform LRQMC again. Simulation results demonstrate that the proposed D-LRQMC can improve restoration accuracy and visual quality for various images compared to the conventional LRQMC. These results suggest the effectiveness of the depth information for color image processing in quaternion domain.

Related papers

• Enhancing Underwater Imaging with 4-D Light Fields: Dataset and Method [77.80712860663886]
  4-D light fields (LFs) enhance underwater imaging plagued by light absorption, scattering, and other challenges. We propose a progressive framework for underwater 4-D LF image enhancement and depth estimation. We construct the first 4-D LF-based underwater image dataset for quantitative evaluation and supervised training of learning-based methods.
  arXiv  Detail & Related papers  (2024-08-30T15:06:45Z)
• Q-SLAM: Quadric Representations for Monocular SLAM [85.82697759049388]
  We reimagine volumetric representations through the lens of quadrics. We use quadric assumption to rectify noisy depth estimations from RGB inputs. We introduce a novel quadric-decomposed transformer to aggregate information across quadrics.
  arXiv  Detail & Related papers  (2024-03-12T23:27:30Z)
• A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration [11.47644299959152]
  We propose a novel quaternion-based WSNM model (QWSNM) for tackling the color image restoration problems. Extensive experiments on two representative CIR tasks, including color image denoising and deblurring, demonstrate that the proposed QWSNM method performs favorably against many state-of-the-art alternatives.
  arXiv  Detail & Related papers  (2023-07-24T09:54:49Z)
• Quaternion tensor left ring decomposition and application for color image inpainting [14.601163837840675]
  We propose the quaternion tensor left ring (QTLR) decomposition, which inherits the powerful and generalized representation abilities of the TR decomposition. The paper further proposes a low-rank quaternion tensor completion (LRQTC) model and its algorithm for color image inpainting based on the defined QTLR decomposition.
  arXiv  Detail & Related papers  (2023-07-20T06:37:47Z)
• Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution [52.582632746409665]
  We propose a novel Symmetric Uncertainty-aware Feature Transmission (SUFT) for color-guided DSR. Our method achieves superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-01T06:35:59Z)
• Quasi Non-Negative Quaternion Matrix Factorization with Application to Color Face Recognition [0.0]
  A novel quasi-negative quaternion matrix factorization (QNQMF) is presented for color image processing. The accuracy of the rate of face recognition on the quaternion model is better than on the red, green and blue channels of color image.
  arXiv  Detail & Related papers  (2022-11-30T04:51:09Z)
• Quaternion Optimized Model with Sparse Regularization for Color Image Recovery [10.137095668835439]
  This paper is inspired by an appreciation of the fact that different signal types, including audio formats and images, possess structures that are inherently sparse in respect of their respective bases. Since color images can be processed as a whole in the quaternion domain, we depicted the sparsity of the color image in the quaternion discrete cosine transform (QDCT) domain. To achieve a more superior low-rank approximation, the quatenrion-based truncated nuclear norm (QTNN) is employed in the proposed model.
  arXiv  Detail & Related papers  (2022-04-19T03:07:12Z)
• Quaternion-based dynamic mode decomposition for background modeling in color videos [1.0098114696565863]
  Dynamic mode decomposition (DMD) is a recently proposed method to robustly decompose a video sequence into the background model and the corresponding foreground part. We propose a quaternion-based DMD (Q-DMD), which extends the DMD by quaternion matrix analysis. We exploit the standard eigenvalues of the quaternion matrix to compute its spectral decomposition and calculate the corresponding Q-DMD modes and eigenvalues.
  arXiv  Detail & Related papers  (2021-12-28T03:35:39Z)
• BOP Challenge 2020 on 6D Object Localization [56.591561228575635]
  The BOP Challenge 2020 is the third in a series of public competitions organized with the goal to capture the status quo in the field of 6D object pose estimation from an RGB-D image. The participants were provided 350K training images generated by BlenderProc4BOP, a new open-source and light-weight physically-based (PBR) and procedural data generator. The top-performing methods rely on RGB-D image channels, but strong results were achieved when only RGB channels were used at both training and test time.
  arXiv  Detail & Related papers  (2020-09-15T22:35:14Z)
• 3D Photography using Context-aware Layered Depth Inpainting [50.66235795163143]
  We propose a method for converting a single RGB-D input image into a 3D photo. A learning-based inpainting model synthesizes new local color-and-depth content into the occluded region. The resulting 3D photos can be efficiently rendered with motion parallax.
  arXiv  Detail & Related papers  (2020-04-09T17:59:06Z)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)

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.