Rotation invariant features for color texture classification and retrieval under varying illumination

Sathyabama, B.; Anitha, M.; Raju, S. M. Taslim Uddin; Abhaikumar, V.

doi:10.1007/s10044-011-0207-0

Cited by 4 publications

(4 citation statements)

References 20 publications

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“…Two phases denote local image shifts and the third phase captures the image texture information. The quaternion wavelet matrix is widely employed in many applications such as image registration [17], edge detection [18,19], image segmentation [20], and classification [21,22]. Recently, quaternion-based methods have extended to process multi-channel information in parallel, such as colour image registration [23], colour image analysis [24], colour image denoising [25][26][27], colour image super-resolution [28], and recognition [29].…”

Section: Introductionmentioning

confidence: 99%

Vector extension of quaternion wavelet transform and its application to colour image denoising

Gai

Bao

Zhang³

2019

IET signal process.

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In this study, the authors study and give a new framework for colour image representation based on colour quaternion wavelet transform (CQWT). The new colour quaternion filter bank is constructed by using radon transform. Starting from link with structure tensors, the authors propose a new multi-scale tool for vector-valued signals which can provide efficient analysis of local features by using the concepts of amplitude, phase, and orientation. To demonstrate the properties of CQWT, new colour image denoising algorithm is proposed by using CQWT and bivariate shrinkage function. The performance of the proposed algorithm is experimentally verified on a variety of noise levels. Experimental results show that the proposed algorithm achieves superior performance both in visual quality and objective peak-signal-to-noise ratio, mean square error, and structure similarity values, compared with other state-of-the-art denoising algorithms. 2 Quaternion wavelet transform In this section, we will review the basic concepts of quaternion wavelet transform. As it was shown in [14], the quaternion wavelet transform of 2D function f (x, y) can be represented as

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Section: Introductionmentioning

confidence: 99%

Vector extension of quaternion wavelet transform and its application to colour image denoising

Gai

Bao

Zhang³

2019

IET signal process.

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“…In fact, there have been several color image filtering methods based on quaternion algebra, where a color image pixel is expressed as a quaternion unit and consequently a color image is formulated as a quaternion matrix. These methods explore new solutions of classical problems, e.g., color image registration [28], color image denoising [27], color image watermarking [29], color image super-resolution [30], image colorization [31] and color image segmentation [32], [33]. For example, global and local windowed hypercomplex Fourier transforms (including quaternion Gabor transform) are proposed to provide spectral analysis of color images [34]- [36].…”

Section: Introductionmentioning

confidence: 99%

Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis

et al. 2015

IEEE Trans. on Image Process.

140

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Traditional sparse image models treat color image pixel as a scalar, which represents color channels separately or concatenate color channels as a monochrome image. In this paper, we propose a vector sparse representation model for color images using quaternion matrix analysis. As a new tool for color image representation, its potential applications in several image-processing tasks are presented, including color image reconstruction, denoising, inpainting, and super-resolution. The proposed model represents the color image as a quaternion matrix, where a quaternion-based dictionary learning algorithm is presented using the K-quaternion singular value decomposition (QSVD) (generalized K-means clustering for QSVD) method. It conducts the sparse basis selection in quaternion space, which uniformly transforms the channel images to an orthogonal color space. In this new color space, it is significant that the inherent color structures can be completely preserved during vector reconstruction. Moreover, the proposed sparse model is more efficient comparing with the current sparse models for image restoration tasks due to lower redundancy between the atoms of different color channels. The experimental results demonstrate that the proposed sparse image model avoids the hue bias issue successfully and shows its potential as a general and powerful tool in color image analysis and processing domain.

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“…With two phases denoting local image shifts and the third phase capturing the image texture information. The QWT is widely used in many applications such as image registration [30], edge detection [31][32][33], image segmentation [34,35], and classification [36][37][38]. In recent years, quaternion-based methods have extended to process multi-channel information in parallel way such as colour image watermarking [39], colour image registration [40], colour image denoising [41], colour image super-resolution [42], colour image segmentation [43,44], and colour image analysis [45].…”

Section: Introductionmentioning

confidence: 99%

Sparse representation based on vector extension of reduced quaternion matrix for multiscale image denoising

Gai

Wang

Yang

et al. 2016

IET Image Processing

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Rotation invariant features for color texture classification and retrieval under varying illumination

Cited by 4 publications

References 20 publications

Vector extension of quaternion wavelet transform and its application to colour image denoising

Vector extension of quaternion wavelet transform and its application to colour image denoising

Vector Sparse Representation of Color Image Using Quaternion Matrix Analysis

Sparse representation based on vector extension of reduced quaternion matrix for multiscale image denoising

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