Combining vector ordering and spatial information for color image interpolation

Jin, Lianghai; Li, Dehua; Song, Enmin

doi:10.1016/j.imavis.2008.06.007

Cited by 12 publications

(9 citation statements)

References 15 publications

(23 reference statements)

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“…The vector directions indicate color chromaticity, and vector magnitude distance specifies noise reduction [2]. The VDDF filter is a nonlinear operator that arranges the distances between the pixels in a local 3x3 window according to the size of their intensity values and replaces the value of the pixel in the result image by the value that minimizes the distance between all the pixels in the window [2] [3]. We considered (…”

Section: Vddf Filtermentioning

confidence: 99%

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FPGA implementation of vector directional distance filter

Boudabous¹,

Atitallah²,

Khriji

et al. 2010

5th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era

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In this paper, we present a fast implementation of the vector directional distance filter (VDDF) for noise suppression and fine-details preservation in color image, based on FPGA hardware/software (HW/SW) environment. For the ease of implementation, we have proposed some approximations. An efficient hardware implementation is developed to acquire best execution time. After validation, using NiosII development board, we demonstrate a clearly improvement in the filtering speed compared to the software solution. Experiments on a large and diverse image sets show that our implementation approach achieves an excellent balance between simplicity, accuracy, and computational speed.

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Section: Vddf Filtermentioning

confidence: 99%

“…The parameter p is a design parameter ranged from 0 to 1. It controls the importance of the angle criterion versus the distance criterion in the overall filter process [3].…”

Section: Vddf Filtermentioning

confidence: 99%

FPGA implementation of vector directional distance filter

Boudabous¹,

Atitallah²,

Khriji

et al. 2010

5th International Conference on Design &Amp; Technology of Integrated Systems in Nanoscale Era

View full text Add to dashboard Cite

show abstract

“…One of the main issues for mathematical morphology is its extension to the multivariable case [1]. So far, a lot of effort has been made to design multivariate morphological operators which can be applied in multichannel images [2][3][4][5]. The key point of constructing morphological operators for multichannel images consists in the definition of the ordering scheme for multivariate data.…”

Section: Introductionmentioning

confidence: 99%

Multivariate Self-Dual Morphological Operators Based on Extremum Constraint

Tao

Wang

Luo

2015

Mathematical Problems in Engineering

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Self-dual morphological operators (SDMO) do not rely on whether one starts the sequence with erosion or dilation; they treat the image foreground and background identically. However, it is difficult to extend SDMO to multichannel images. Based on the self-duality property of traditional morphological operators and the theory of extremum constraint, this paper gives a complete characterization for the construction of multivariate SDMO. We introduce a pair of symmetric vector orderings (SVO) to construct multivariate dual morphological operators. Furthermore, utilizing extremum constraint to optimize multivariate morphological operators, we construct multivariate SDMO. Finally, we illustrate the importance and effectiveness of the multivariate SDMO by applications of noise removal and segmentation performance. The experimental results show that the proposed multivariate SDMO achieves better results, and they suppress noises more efficiently without losing image details compared with other filtering methods. Moreover, the proposed multivariate SDMO is also shown to have the best segmentation performance after the filtered images via watershed transformation.

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“…The above-mentioned methods were proposed for grey images. In the meantime, there are also many methods proposed for color images such as the saliency-directed interpolation [20] and combining vector ordering interpolation [21]. In brief, in spatial domain, [9][10][11][12][13][14][15][16][17][18][19][20][21] cost more computation time than our approach on enlargement processing.…”

Section: Introductionmentioning

confidence: 99%

“…In the meantime, there are also many methods proposed for color images such as the saliency-directed interpolation [20] and combining vector ordering interpolation [21]. In brief, in spatial domain, [9][10][11][12][13][14][15][16][17][18][19][20][21] cost more computation time than our approach on enlargement processing. In addition, most of methods [9][10][11][12][13][14][15][16][17][18][19] cannot deal with non-integer factor enlargement; in frequency domain, [5][6][7][8] do not consider global information in whole image, such as the traditional DCT method.…”

Section: Introductionmentioning

confidence: 99%

Fast large‐scale image enlargement method with a novel evaluation approach: benchmark function‐based peak signal‐to‐noise ratio

Guo¹,

Hsu²,

Kuo³

et al. 2015

IET Image Processing

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An objective novel evaluation approach, implemented by the benchmark function-based peak signal-to-noise ratio, particularly suitable for evaluating the performance of a large-scale enlargement of a small size image is proposed in this study. Also, a fast large-scale image enlargement method via the improved discrete cosine transform (DCT) is proposed to improve the quality and speed of image zooming. The proposed image enlargement algorithm based on DCT saves computation time by multiplication of the DCT matrix. Compared with the traditional DCT approach, the improved approach overcomes the image shifting and blocky effects. In comparisons with other interpolation methods, DCT enlargement outperforms them in edge details because it considers the global frequency information of the whole image. With the DCT enlargement, it is easy to implement the arbitrary pixel-size-based zooming of an image by employing the different size of transform matrix. Illustrative examples show the effectiveness of the proposed approach.

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Combining vector ordering and spatial information for color image interpolation

Cited by 12 publications

References 15 publications

FPGA implementation of vector directional distance filter

FPGA implementation of vector directional distance filter

Multivariate Self-Dual Morphological Operators Based on Extremum Constraint

Fast large‐scale image enlargement method with a novel evaluation approach: benchmark function‐based peak signal‐to‐noise ratio

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