New colour morphological operators on hypergraph

Wang, Junping; Liang, Gangming; Wu, Yao; Li, Yong; Hu, Jing

doi:10.1049/iet-ipr.2017.0468

Cited by 3 publications

(8 citation statements)

References 16 publications

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“…In this section, we show the experimental results of noise removal from color images by the proposed order space-based morphological operations, which are compared with the state-of-the-art method of Wang et al [19]. Figure 6 shows a noisy input image including 10% impulse noise.…”

Section: Resultsmentioning

confidence: 99%

“…Figure 6 shows a noisy input image including 10% impulse noise. Figure 7 shows the output images by Wang's hypergraph-based method [19] and the proposed method with 'Cross' and 'Square' structuring elements in Figure 1a and Figure 1b, respectively, where the order o S in (7) is used. Wang's method has two parameters, µ and β, which are set as µ = 250 and β = 1 by adjusting for this noise removal task through preliminary experiments.…”

Section: Resultsmentioning

confidence: 99%

“…Lezoray et al proposed a method for the graph-based ordering of color vectors, and demonstrated the proposed mathematical morphology operations in RGB, IHSL [16], L * a * b * and CIECAM02 [17] color models [18]. Wang et al proposed color morphological operators on a hypergraph to avoid the loss of details caused by fixed structuring elements [19]. The hypergraph-based method has two parameters for controlling spatial and color similarities between pixels.…”

Section: Introductionmentioning

confidence: 99%

“…After that, we compute a single order from the obtained triplet of orders by a function from 3D to 1D, and then apply morphological operations based on the single order of pixels. In our experiments, we compere the proposed method with Wang's hypergraph-based method [19], and demonstrate the performance of the proposed method in a noise-removal task. Moreover, we present the fuzzy version of the proposed order space-based method, which improves the performance further.…”

Section: Introductionmentioning

confidence: 99%

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Order Space-Based Morphology for Color Image Processing

et al. 2023

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Mathematical morphology is a fundamental tool based on order statistics for image processing, such as noise reduction, image enhancement and feature extraction, and is well-established for binary and grayscale images, whose pixels can be sorted by their pixel values, i.e., each pixel has a single number. On the other hand, each pixel in a color image has three numbers corresponding to three color channels, e.g., red (R), green (G) and blue (B) channels in an RGB color image. Therefore, it is difficult to sort color pixels uniquely. In this paper, we propose a method for unifying the orders of pixels sorted in each color channel separately, where we consider that a pixel exists in a three-dimensional space called order space, and derive a single order by a monotonically nondecreasing function defined on the order space. We also fuzzify the proposed order space-based morphological operations, and demonstrate the effectiveness of the proposed method by comparing with a state-of-the-art method based on hypergraph theory. The proposed method treats three orders of pixels sorted in respective color channels equally. Therefore, the proposed method is consistent with the conventional morphological operations for binary and grayscale images.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Order Space-Based Morphology for Color Image Processing

et al. 2023

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“…Some works propose the automatic reference color selection for MM [38], [39]. Wang et al [40] induce the RGB color distance and establish the structural hypergraph in color images. In the L * a * b * space, the Euclidean norm (distance to the origin L = 0, a = 0 and b = 0) is often used as an ordering method for pixels to evaluate the quality of color reproduction, or in color image compression techniques [41].…”

Section: Introductionmentioning

confidence: 99%

Adaptive RGB Color Lexicographical Ordering Framework Using Statistical Parameters From the Color Component Histogram

2019

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In image filtering, the classical lexicographical ordering is a popular method that cannot be directly applied for ordering colors in RGB color images. This is due to the fact that each color has similar importance and no order can be defined trivially a priori. In this work we propose an adaptive color lexicographical ordering framework for RGB color images where a color pixel is transformed into a real number. This transformation is weighted by statistical parameters from each color component histogram and used as the main component for color comparison. This approach seeks to avoid the arbitrariness since the order of the color component priorities is defined by the information extracted from the image itself. The proposed approach was tested by applying a median filter to reduce noise and a morphological approach to local contrast enhancement. In noise reduction, we compare our method with classical ordering techniques on images with different noise levels. Results show that our proposal outperformed the state-of-the art methods according to the Mean Absolute Error (MAE) measure, especially in those scenarios with higher noise levels. In contrast enhancement, the proposed framework outperformed the classical lexicographical ordering method according to Contrast Improvement Ratio (CIR) metric, especially when increasing the contrast factor. Our proposal generates less distortion than the state-of-the art methods ordering.

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