Quaternion higher-order singular value decomposition and its applications in color image processing

• URL: http://arxiv.org/abs/2101.00364v1
• Date: Sat, 2 Jan 2021 03:54:56 GMT
• Title: Quaternion higher-order singular value decomposition and its applications in color image processing
• Authors: Jifei Miao and Kit Ian Kou
• Abstract summary: We generalize the HOSVD to the quaternion domain and define quaternion-based HOSVD (QHOSVD) Due to the non-commutability of quaternion multiplication, QHOSVD is not a trivial extension of the HOSVD. We present two applications of the defined QHOSVD in color image processing: multi_focus color image fusion and color image denoising.
• Score: 1.1929584800629671
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Higher-order singular value decomposition (HOSVD) is one of the most efficient tensor decomposition techniques. It has the salient ability to represent high_dimensional data and extract features. In more recent years, the quaternion has proven to be a very suitable tool for color pixel representation as it can well preserve cross-channel correlation of color channels. Motivated by the advantages of the HOSVD and the quaternion tool, in this paper, we generalize the HOSVD to the quaternion domain and define quaternion-based HOSVD (QHOSVD). Due to the non-commutability of quaternion multiplication, QHOSVD is not a trivial extension of the HOSVD. They have similar but different calculation procedures. The defined QHOSVD can be widely used in various visual data processing with color pixels. In this paper, we present two applications of the defined QHOSVD in color image processing: multi_focus color image fusion and color image denoising. The experimental results on the two applications respectively demonstrate the competitive performance of the proposed methods over some existing ones.

Related papers

• A New Cross-Space Total Variation Regularization Model for Color Image Restoration with Quaternion Blur Operator [20.00683294783224]
  Cross-channel deblurring problem in color image processing is difficult to solve due to complex coupling and structural blurring of color pixels. We present a novel cross-space total variation (CSTV) regularization model for color image deblurring. New L-curve method is proposed to find a sweet balance of regularization functionals on different color spaces.
  arXiv  Detail & Related papers  (2024-05-20T15:29:26Z)
• Color Equivariant Convolutional Networks [50.655443383582124]
  CNNs struggle if there is data imbalance between color variations introduced by accidental recording conditions. We propose Color Equivariant Convolutions ( CEConvs), a novel deep learning building block that enables shape feature sharing across the color spectrum. We demonstrate the benefits of CEConvs in terms of downstream performance to various tasks and improved robustness to color changes, including train-test distribution shifts.
  arXiv  Detail & Related papers  (2023-10-30T09:18:49Z)
• DARC: Distribution-Aware Re-Coloring Model for Generalizable Nucleus Segmentation [68.43628183890007]
  We argue that domain gaps can also be caused by different foreground (nucleus)-background ratios. First, we introduce a re-coloring method that relieves dramatic image color variations between different domains. Second, we propose a new instance normalization method that is robust to the variation in the foreground-background ratios.
  arXiv  Detail & Related papers  (2023-09-01T01:01:13Z)
• 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)
• Name Your Colour For the Task: Artificially Discover Colour Naming via Colour Quantisation Transformer [62.75343115345667]
  We propose a novel colour quantisation transformer, CQFormer, that quantises colour space while maintaining machine recognition on the quantised images. We observe the consistent evolution pattern between our artificial colour system and basic colour terms across human languages. Our colour quantisation method also offers an efficient quantisation method that effectively compresses the image storage.
  arXiv  Detail & Related papers  (2022-12-07T03:39:18Z)
• 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)
• Saliency Enhancement using Superpixel Similarity [77.34726150561087]
  Saliency Object Detection (SOD) has several applications in image analysis. Deep-learning-based SOD methods are among the most effective, but they may miss foreground parts with similar colors. We introduce a post-processing method, named textitSaliency Enhancement over Superpixel Similarity (SESS) We demonstrate that SESS can consistently and considerably improve the results of three deep-learning-based SOD methods on five image datasets.
  arXiv  Detail & Related papers  (2021-12-01T17:22:54Z)
• Generalized Two-Dimensional Quaternion Principal Component Analysis with Weighting for Color Image Recognition [1.516937009186805]
  One of the most powerful methods of color image recognition is the two-dimensional principle component analysis (2DQPCA) approach. In this paper, a generalized 2DQPCA approach with weighting is presented with imposing $L_p$ norms on both constraint and objective functions. The numerical results based on the real face databases validate that the newly proposed method is robust to noise and performs better than the state-of-the-art 2DQPCA-based algorithms and four prominent deep learning methods.
  arXiv  Detail & Related papers  (2020-10-04T03:37:23Z)
• Uncertainty Inspired RGB-D Saliency Detection [70.50583438784571]
  We propose the first framework to employ uncertainty for RGB-D saliency detection by learning from the data labeling process. Inspired by the saliency data labeling process, we propose a generative architecture to achieve probabilistic RGB-D saliency detection. Results on six challenging RGB-D benchmark datasets show our approach's superior performance in learning the distribution of saliency maps.
  arXiv  Detail & Related papers  (2020-09-07T13:01:45Z)
• Full Quaternion Representation of Color images: A Case Study on QSVD-based Color Image Compression [0.38073142980732994]
  We propose an approach for representing color images with full quaternion numbers. An autoencoder neural network is used to generate a global model for transforming a color image into a full quaternion matrix.
  arXiv  Detail & Related papers  (2020-07-19T19:13:21Z)
• Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation [59.94819184452694]
  Depth information has proven to be a useful cue in the semantic segmentation of RGBD images for providing a geometric counterpart to the RGB representation. Most existing works simply assume that depth measurements are accurate and well-aligned with the RGB pixels and models the problem as a cross-modal feature fusion. In this paper, we propose a unified and efficient Crossmodality Guided to not only effectively recalibrate RGB feature responses, but also to distill accurate depth information via multiple stages and aggregate the two recalibrated representations alternatively.
  arXiv  Detail & Related papers  (2020-07-17T18:35:24Z)

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.