Lianghai Jin

A switching vector median filter based on the CIELAB color space for color image restoration

Vector median filter is highly effective in removing impulsive noise from color images. However, it fails to distinguish thin lines and boundaries from impulsive noise, and usually filters them out because it interprets these fine details as some noise. This paper introduces a new solution for impulsive noise detection in color images. The proposed solution is a switching vector filter which analyzes the color difference of two pixels in the CIELAB color space using four directional operators. Based on the result of this analysis, the impulse detection module can identify noise pixels, which are replaced in the filtering module with some robust estimate. The extensive experimental results show that the proposed solution outperforms many of the existing vector filters in terms of the filtering performance. In particular, the proposed approach can effectively preserve the thin lines, fine details, and image edges.

Color impulsive noise removal based on quaternion representation and directional vector order-statistics

A new method for detecting and suppressing impulsive noise in color images is presented in this paper. The proposed method is a type of switching vector filters, where the impulse detection is based on the order-statistic information about the color samples in the horizontal, vertical, and diagonal directions. The new solution first uses quaternion-based representation of color differences and median deviation-based techniques to search for the edge direction with the maximum number of similar pixels, and then utilizes the samples aligning with this edge direction to judge whether the current pixel is noisy or not and control the switching between identity (no filtering) and vector median filtering actions. Extensive experimental comparisons exhibit the validity of the proposed approach by showing significant performance improvements over other well-known color image filtering techniques.

Unsupervised color-texture segmentation based on multiscale quaternion Gabor filters and splitting strategy

This paper proposes a new method for color-texture segmentation based on a splitting framework with graph cut technique. To process the scale difference of quaternion Gabor filter (QGF) features of a color textured image, a new multiscale QGF (MQGF) is introduced to describe texture attributes of the given image. Then, the segmentation is formulated in terms of energy minimization gradually obtained using binary graph cuts, where color and MQGF features are modeled with a multivariate finite mixture model, and minimum description length (MDL) principle is integrated into this framework as a splitting criterion. In contrast to previous approaches, our method finds an optimal segmentation by balancing energy cost and coding length, and the segmentation result is determined during the splitting process automatically. Experimental results on both synthetic and real natural color textured images demonstrate the good performance of the proposed method.

Two-stage quaternion switching vector filter for color impulse noise removal

In this paper, a new approach to impulse noise removal in color images is presented. The proposed solution is a quaternion switching vector filter in which the impulse detection consists of two stages. First, by using quaternion representation, an effective color distance measure method is developed. Then, based on the new color distance measure, the proposed filter utilizes the directional samples along four directions to classify image pixels into possible noisy and noise-free ones. For possible noisy pixels, the concept of peer group is modified and extended to the directional samples to further detect whether they are corrupted by impulse noise or not. Finally, a weighted vector median filter is performed only on the pixels that are identified as noisy by the second stage. The experimental comparisons exhibit the validity of the proposed solution by showing significant performance improvements over other well-known color image filtering methods. An effective two-stage impulse noise detection method.A new color distance measure method based on quaternion representation.Median vector based peer group for detecting noisy directional samples.

Label fusion method based on sparse patch representation for the brain MRI image segmentation

The multi-Atlas patch-based label fusion method (MAS-PBM) has emerged as a promising technique for the magnetic resonance imaging (MRI) image segmentation. The state-of-the-art MAS-PBM approach measures the patch similarity between the target image and each atlas image using the features extracted from images intensity only. It is well known that each atlas consists of both MRI image and labelled image (which is also called the map). In other words, the map information is not used in calculating the similarity in the existing MAS-PBM. To improve the segmentation result, the authors propose an enhanced MAS-PBM in which the maps will be used for similarity measure. The first component of the proposed method is that an initial segmentation result (i.e. an appropriate map for the target) is obtained by using either the non-local-patch-based label fusion method (NPBM) or the sparse patch-based label fusion method (SPBM) based on the grey scales of patches. Then, the SPBM is applied again to obtain the finer segmentation based on the labels of patches. The authors called these two versions of the proposed fusion method as MAS-PBM-NPBM and MAS-PBM-SPBM. Experimental results show that more accurate segmentation results are achieved compared with those of the majority voting, NPBM, SPBM, STEPS and the hierarchical multi-atlas label fusion with multi-scale feature representation and label-specific patch partition.

A quaternion-based spectral clustering method for color image segmentation

Spectral clustering method has been widely used in image segmentation. A key issue in spectral clustering is how to build the affinity matrix. When it is applied to color image segmentation, most of the existing methods either use Euclidean metric to define the affinity matrix, or first converting color-images into gray-level images and then use the gray-level images to construct the affinity matrix (component-wise method). However, it is known that Euclidean distances can not represent the color differences well and the component-wise method does not consider the correlation between color channels. In this paper, we propose a new method to produce the affinity matrix, in which the color images are first represented in quaternion form and then the similarities between color pixels are measured by quaternion rotation (QR) mechanism. The experimental results show the superiority of the new method.

An improved label fusion approach with sparse patch-based representation for MRI brain image segmentation

The multi‐atlas patch‐based label fusion (LF) method mainly focuses on the measurement of the patch similarity which is the comparison between the atlas patch and the target patch. To enhance the LF performance, the distribution probability about the target can be used during the LF process. Hence, we consider two LF schemes: in the first scheme, we keep the results of the interpolation so that we can obtain the labels of the atlas with discrete values (between 0 and 1) instead of binary values in the label propagation. In doing so, each atlas can be treated as a probability atlas. Second, we introduce the distribution probability of the tissue (to be segmented) in the sparse patch‐based LF process. Based on the probability of the tissue and sparse patch‐based representation, we propose three different LF methods which are called LF‐Method‐1, LF‐Method‐2, and LF‐Method‐3. In addition, an automated estimation method about the distribution probability of the tissue is also proposed. To evaluate the accuracy of our proposed LF methods, the methods were compared with those of the nonlocal patch‐based LF method (Nonlocal‐PBM), the sparse patch‐based LF method (Sparse‐PBM), majority voting method, similarity and truth estimation for propagated segmentations, and hierarchical multi‐atlas LF with multi‐scale feature representation and label‐specific patch partition (HMAS). Based on our experimental results and quantitative comparison, our methods are promising in the magnetic resonance image segmentation.

An integrated similarity metric for graph-based color image segmentation

Graph-based method has become one of the major trends in image segmentation. In this paper, we focus on how to build the affinity matrix which is one of the key issues in graph-based color image segmentation. Four different metrics are integrated in order to build an effective affinity matrix for segmentation. First, the quaternion-based color distance is utilized to measure color differences between color pixels and the oversegmented regions (superpixels), which is more accurate than the commonly used Euclidean distance. In order to describe the superpixels well, especially for texture images, we combine the mean and the variance information to represent the superpixels. Then the image boundary information is used to merge the oversegmented regions to preserve the image edge and reduce the computational complexity. An object for recognition may be cut into nonadjacent sub-parts by clutter or shadows, the affinities between adjacent and nonadjacent superpixels are computed in our study. This feature of affinity is not considered in other methods which only consider the similarity of adjacent regions. Experimental results on the Berkeley segmentation dataset (BSDS) and Weizmann segmentation evaluation datasets demonstrate the superiority of the proposed approach compared with some existing popular image segmentation methods.

An effective vector filter for impulse noise reduction based on adaptive quaternion color distance mechanism

Abstract The measure of color distances plays an important role in color image processing. An effective color distance method is based on quaternion representation, which computes a color distance by weighted average of the distances of luminance and quaternion chromaticity. However, the mechanism of assigning fixed weights to luminance and chromaticity distances cannot always effectively measure color distances, since in a color image chromaticity can change significantly. To address this issue, this paper proposes an adaptive weighted quaternion color distance method. Based on the new color distance measure, the robust outlyingness ratio and local reachability density, which are defined in grayscale images, are extended to color images to implement a coarse-to-fine color noise detection operator. In noise filtering, a weighted vector median filter is employed to restore the pixels judged as noisy. Experimental results exhibit the validity of the proposed method by showing better performance in terms of both objective criteria and visual effect, compared to other widely-used color image filtering methods.

Color image sharpening based on local color statistics

This paper presents an effective color image sharpening method, which is based on local color statistics. First, the variance of a set of color samples is measured by a scalar that is computed based on the sum of distances of color vectors, whereas other studies usually treat a color variance as a 3D vector. This is because what a variance expresses is the degree of the deviation of the image (vector) signal from its mean, indicating that describing this degree of deviation by a scalar is reasonable. Then, the local scalar variance and mean vector are combined together to measure the change of color image signal from a pixel to its neighboring ones, and the polarity of the change is determined by the change of luminance. Finally, based on the measure of the change, an effective sharpening operator is developed. Experimental results show that the proposed method excellently sharpens different kinds of color images and at the same time preserves image chromaticity well, and outperforms other typical sharpening techniques in both objective assessment and visual evaluation.