Real Color Images Research Articles

Real Color Image Denoising Using t-Product- Based Weighted Tensor Nuclear Norm Minimization

Color images can be seen as third-order tensors with column, row and color modes. Considering two inherent characteristics of a color image including the non-local self-similarity (NSS) and the cross-channel correlation, we extract non-local similar patch groups from a color image and treat these groups as tensors with each color channel corresponding to the frontal slice of the tensor to exploit the information within and cross channel correlation. Inspired by recently proposed tensor-tensor product (t-product), t-SVD, tensor tubal rank and rigorously deduced tensor nuclear norm, a novel t-product based weighted tensor nuclear norm minimization (WTNNM) is proposed to model the extracted non-local similar patch group tensor (NPGT). Considering the NPGT is of low tubal rank, we formulate real color image denoising as a low tubal rank tensor recovery problem and solve it with the weighted tensor nuclear norm minimization. Experiments on both simulated and realistic noisy images verify the effectiveness of our method.

Split Bregman method based level set formulations for segmentation and correction with application to MR images and color images

At present, magnetic resonance (MR) images have gradually become a major aid for clinical medicine, which has greatly improved the doctor's diagnosis rate. Accurate and fast segmentation of MR images plays an extremely important role in medical research. However, due to the influence of external factors and the defects of imaging devices, the MR images have severe intensity inhomogeneity, which poses a great challenge to accurately segment MR images. To deal with this problem, this paper presents an improved active contour model by combining the level set evolution model (LSE) and the split Bregman method, and gives the two-phase, the multi-phase and the vector-valued formulations of our model, respectively. The use of the split Bregman method accelerates the minimization process of our model by reducing the computation time and iterative times. A slowly varying bias field is added into the energy functional, which is the key to correct inhomogeneous images. By estimating the bias fields, not only can we get accurate image segmentation results, but also a homogeneous image after correction is provided. Then we apply our model to segment a large amount of synthetic and real MR images, including gray and color images. Experimental results show that our model can provide satisfactory segmentation and correction results for both gray and color images. Besides, compared with the LSE model, our model has higher accuracy and is superior to the LSE model. In addition, experimental results also demonstrate that our model has the advantages of being insensitive to initial contours and robust to noises.

Image objects detection with local topological characteristics, obtained by two-dimensional variations

I proposed method for producing local topological image characteristics on the base of two–dimensional variations. This method uses variations-based estimates and forms the metrical parameters of the objects of observed scene. I offered new concepts of the object size index and the convexity amplitude indices up and down. I stated algorithm for calculation of these characteristics. Application of these indices renders the new approach for object detection in an image. I demonstrated results of object detection in real gray scale and color images.

Approach for sequential image interpretation using a priori binary perceptual topological and photometric knowledge and k-means-based segmentation.

The proposed approach exploits a priori known qualitative inclusion and photometric relationships between image regions, represented by oriented graphs. Our work assumes a sequential image segmentation procedure where regions are progressively segmented and recognized by associating them with corresponding nodes in graphs related to the prior knowledge. The main contribution concerns the parameterization of the k-means clustering algorithm, to be used during the segmentation procedure, and the graph-matching-based identification of resulting clusters, corresponding to regions declared in graphs. The parameterization of k-means is based on known relationships as well as on regions that have been segmented and recognized at previous steps. Parameters are the region of interest within which k-means clustering is constrained, the number of clusters, and seeding constraints. Photometric relationships built from resulting clusters are matched with a priori known relationships to identify each cluster, this being formulated as an exact graph-matching problem. The potential of this approach is studied in four use cases involving real gray-scale and color images with dedicated sequential analysis procedures. Processing results are compared with those obtained without the proposed parameterization of k-means, as well as with some other clustering approaches. Results show the relevance of our approach, in particular in terms of segmentation accuracy, computation time, and seeding reliability.

Color image restoration and inpainting via multi-channel total curvature

The multi-channel total variation (MTV) based on L2 norm is capable of preserving object edges and smoothing flat regions in color images. However, it will lead to loss of image contrast, smear object corners, and produce staircase artifacts in the restored images. In order to remedy these side effects, we propose a new multi-channel total curvature model based on L1 norm (MTC-L1) for vector-valued image restoration in this paper. By introducing some auxiliary variables and Lagrange multipliers, we develop a fast algorithm based alternating direction method of multipliers (ADMM) for the proposed model, which allows the use of the fast Fourier transform (FFT), generalized soft threshold formulas and projection method. Extensive experiments have been conducted on both synthetic and real color images, which validate the proposed approach for better restoration performance, and show advantages of the proposed ADMM over algorithms based on traditional gradient descent method (GDM) in terms of computational efficiency.

A Fuzzy Clustering Approach for Complex Color Image Segmentation Based on Gaussian Model with Interactions between Color Planes and Mixture Gaussian Model

In complex color images, colors inside a homogeneous region might be contradistinctive and the distribution could not be described by a simple Gaussian distribution as used in traditional image segmentation algorithms. Based on the characteristics that the red, green, and blue color planes are not independent and pixels in the same neighborhood system might stand for the same object, we introduce a Gaussian model containing the interactions between different color planes to strengthen the connections both on a color plane and between color planes in a neighborhood system. Consequently, a Gaussian mixture model with the prior distribution, defined by Markov random field and acting as the weight, is employed to describe the distribution of color measures inside a homogeneous region. With the Gaussian mixture model containing the interactions between color planes, we proposed a fuzzy clustering approach for complex color image segmentation. Experiments on synthetic and real-color images, in which homogeneous regions are complex, show that the proposed algorithm compares favorably with the compared algorithms developed for the same purpose.

An oscillatory network model with controllable synchronization and a neuromorphic dynamical method of information processing

A spatially two-dimensional oscillatory neural network model with inhomogeneous modifiable oscillatory coupling is designed and an adaptive dynamical method of brightness image segmentation (image reconstruction) based on self-organized cluster synchronization in the oscillatory network is developed. The method imitates the known phenomenon of dynamical binding via synchronization that is presumably used by a number of the brain neural structures in their work. The oscillatory-network approach demonstrates the following capabilities: (1) high-quality segmentation of real grey-level and color images; (2) selective image segmentation (exclusion of unnecessary information); (3) solution of the simplest problem of object selection in a visual scene—the problem of the successive selection of all spatially separated image fragments of almost equal brightness.

Extraordinary perceptual color stability in low cost, real time color image compression inspired by structure tensor analysis

Extraordinary perceptual color stability in low cost, real time color image compression inspired by structure tensor analysis

Efficient quantum inspired meta-heuristics for multi-level true colour image thresholding

Thresholding is a commonly used simple and effective technique for image segmentation. The computational time in multi-level thresholding significantly increases with the level of computation because of exhaustive searching, adding to exponential growth of computational complexity. Hence, in this paper, the features of quantum computing are exploited to introduce four different quantum inspired meta-heuristic techniques to accelerate the execution of multi-level thresholding. The proposed techniques are Quantum Inspired Genetic Algorithm, Quantum Inspired Simulated Annealing, Quantum Inspired Differential Evolution and Quantum Inspired Particle Swarm Optimization. The effectiveness of the proposed techniques is exhibited in comparison with the backtracking search optimization algorithm, the composite DE method, the classical genetic algorithm, the classical simulated annealing, the classical differential evolution and the classical particle swarm optimization for ten real life true colour images. The experimental results are presented in terms of optimal threshold values for each primary colour component, the fitness value and the computational time (in seconds) at different levels. Thereafter, the quality of thresholding is judged in terms of the peak signal-to-noise ratio for each technique. Moreover, statistical test, referred to as Friedman test, and also median based estimation among all techniques, are conducted separately to judge the preeminence of a technique among them. Finally, the performance of each technique is visually judged from convergence plots for all test images, which affirms that the proposed quantum inspired particle swarm optimization technique outperforms other techniques.

Active contours textural and inhomogeneous object extraction

A new selective segmentation active contour model is proposed in this paper that embeds an enhanced image information. By utilizing the average image of channels (AIC), which handles texture and noise, our model is capable to selectively segment and capture objects with nonuniform features. Moreover, the AIC is fitted with linear functions which are updated regularly to accurately guide the level set function to handle nonconstant intensities. Furthermore, we employ prior information in terms of geometrical constraints which work in alliance with image information to capture objects with intensity inhomogeneity. Experiments show that the proposed method achieves better results than the latest selective segmentation models. In addition, our approach maintains the performance on some hard real and synthetic color images.

New quantum inspired meta-heuristic techniques for multi-level colour image thresholding

The efficient meta-heuristic techniques, called ant colony optimization, differential evolution and particle swarm optimization, inspired by the fundamental features of quantum systems, are presented in this paper. The proposed techniques are Quantum Inspired Ant Colony Optimization, Quantum Inspired Differential Evolution and Quantum Inspired Particle Swarm Optimization for Multi-level Colour Image Thresholding. These techniques find optimal threshold values at different levels of thresholding for colour images. A minimum cross entropy based thresholding method, called Li's method is employed as an objective (fitness) function for this purpose. The efficiency of the proposed techniques is exhibited computationally and visually on ten real life true colour images. Experiments with the composite DE (CoDE) method, the backtracking search optimization algorithm (BSA), the classical ant colony optimization (ACO), the classical differential evolution (DE) and the classical particle swarm optimization (PSO), have also been conducted subsequently along with the proposed techniques. Experimental results are described in terms of the best threshold value, fitness measure and the computational time (in seconds) for each technique at various levels. Thereafter, the accuracy and stability of the proposed techniques are established by computing the mean and standard deviation of fitness values for each technique. Moreover, the quality of thresholding for each technique is determined by computing the peak signal-to-noise ratio (PSNR) values at different levels. Afterwards, the statistical superiority of the proposed techniques is proved by incorporating Friedman test (statistical test) among different techniques. Finally, convergence curves for different techniques are presented for all test images to show the visual representation of results, which proves that the proposed Quantum Inspired Ant Colony Optimization technique outperforms all the other techniques.

All-in-focus and multi-focus color image reconstruction from a database of color and depth image pairs

Owing to recent advances in depth sensors and computer vision algorithms, depth images are often available with co-registered color images. In this paper, we propose a simple but effective method for obtaining an all-in-focus (AIF) color image from a database of color and depth image pairs. Since the defocus blur is inherently depth-dependent, the color pixels are first grouped according to their depth values. The defocus blur parameters are then estimated using the amount of the defocus blur of the grouped pixels. Given a defocused color image and its estimated blur parameters, the AIF image is produced by adopting the conventional pixel-wise mapping technique. In addition, the availability of the depth image disambiguates the objects located far or near from the in-focus object and thus facilitates image refocusing. We demonstrate the effectiveness of the proposed algorithm using both synthetic and real color and depth images.

Detection of Edges in Color Images

Edge detection considers the important technical details of digital image processing. Many edge detection operators already perform edge detection in digital color imaging. In this study, the edge of many real color images that represent the type of digital image was detected using a new operator in the least square approximation method, which is a type of numerical method. The Linear Fitting algorithm is computationally more expensive compared to the Canny, LoG, Sobel, Prewitt, HIS, Fuzzy, Parametric, Synthetic and Vector methods, and Robert’ operators. The results showed that the new method can detect an edge in a digital color image with high efficiency compared to standard methods used for edge detection. In addition, the suggested operator is very useful for detecting the edge in a digital color image.

A new human heart vessel identification, segmentation and 3D reconstruction mechanism.

BackgroundThe identification and segmentation of inhomogeneous image regions is one of the most challenging issues nowadays. The surface vessels of the human heart are important for the surgeons to locate the region where to perform the surgery and to avoid surgical injuries. In addition, such identification, segmentation, and visualisation helps novice surgeons in the training phase of cardiac surgery.MethodsThis article introduces a new mechanism for identifying the position of vessels leading to the performance of surgery by enhancement of the input image. In addition, develop a 3D vessel reconstruction out of a single-view of a real human heart colour image obtained during open-heart surgery.ResultsReduces the time required for locating the vessel region of interest (ROI). The vessel ROI must appear clearly for the surgeons. Furthermore, reduces the time required for training cardiac surgery of the novice surgeons. The 94.42% accuracy rate of the proposed vessel segmentation method using RGB colour space compares to other colour spaces.ConclusionsThe advantage of this mechanism is to help the surgeons to perform surgery in less time, avoid surgical errors, and to reduce surgical effort. Moreover, the proposed technique can reconstruct the 3D vessel model from a single image to facilitate learning of the heart anatomy as well as training of cardiac surgery for the novice surgeons. Furthermore, extensive experiments have been conducted which reveal the superior performance of the proposed mechanism compared to the state of the art methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s13019-014-0161-1) contains supplementary material, which is available to authorized users.

Analysis of Mixture Formation and Flame Development in Biodiesel Burner Combustion Using Direct Optical Visualization Technique <sup></sup>

Despite years of emission-reduction attempts, biodiesel combustion still have problem of emitting NOx, CO and particulate matter (PM) into the atmosphere. To reduce emissions, the improvements throughout the combustion process have been considered with the enhancement fuel-air mixing. Optical visualization technique is very essential due to its ability to record real time color image of fuel-air premixing and flame development during burning process. The purpose of this study is to determine the relation between mixture formation and flame development of burner combustion using optical visualization technique and image processing technique. Blending of biodiesel ratio was varied from 5 vol% (B5) – 15 vol% (B15). Water content under emulsified biodiesel was varied up to 15 percent, and equivalence ratio from 0.6 to 2.0. The real spray image of emulsified biodiesel and different equivalence ratio were captured by direct photography method together with color digital camera. The images of the spatial distribution of fuel-air mixing and natural flame luminosity will be further analyzed and to obtain information of the flame development in order to understand the influences of fuel-air premixing on combustion process and burning process. Analysis of spray evaporation and observations of combustion process reveal that the mixture formation exhibit influences to the ignition process and flame development.

An efficient and accurate approach of circular object detection in color images

Object detection is a critical step of many image recognition systems. In this paper, we discuss the problem of circular shape object detection in still color images. An isotropic edge detector merged with spatial information and region based analysis is employed to extract image edge and obtain accurate gradient information of edge pixels, which assures the high accuracy of the subsequent circle detection. Then three circle parameters are estimated efficiently with only one 2-dimensional accumulator array and one 1-dimensional accumulator array, which greatly reduces the storage requirements and time complexity of our approach. Experimental results show that our method is robust in locating objects with complete or incomplete or concentric circle boundary in real color images without any prior knowledge.

A Blind Watermarking Technique for Color Image based on SVD with Circulation

Digital watermarking is a method for protecting copyrighted materials such as digital images. This paper presents a new color watermark embedding technique with circulation, based on non-overlapping Singular Value Decomposition (SVD) for hiding important information in images. First, the real-color original image is decomposed into three components R, G and B. The blue component and the watermark performed by Chebyshev chaotic map are divided into non-overlapping blocks, respectively. Then an SVD is performed on each partitioned blocks. Second, the Singular Values (SVs) of watermark are embedded into the singular value matrix of blue component with circulation. Extracting any consecutive four rows and columns from the blocked watermarked image can get complete watermark information. Every watermark bits have been embedded several times and their embedding position are consecutive in the selected original image. That is why, our proposed scheme is naturally secure to geometric attacks and compound attacks, and it is also strongly robust against common image processing attacks. Index Terms—Copyrighted materials, Circulation, Chebyshev chaotic map, Singular values

Study on Recognition of Black Insects on Dark Background by Computer Vision

Improved color channel comparison method (ICCCM) is an effective method to transformcolor images into gray-scale ones. Based on the ICCCM, black or white insects could be effectively extracted and recognized from the real color images with bright background. Howeverit is difficult to use the ICCCM to extract and recognize the black insects from the realcolorimage with dark background. In this paper, the ICCCM is modified to transformthe color images into the gray ones, extracting and recognizing the black insectson the dark background. The ICCCM is modified as follows: (1) A threshold of the gray image is an average brightness value ofred (R), green (G) and blue (B) in all the image pixels.(2) The bright pixels and the color pixels have the highest brightness value 255 in the gray image.(3) A pixel brightness value of the dark area in the gray image equals to a minimum of R, G and B in the pixel. (4) After deleted all the pixels with a brightness value of 255, a threshold of the binary image is determined by Otsus theory. The modified ICCCM more effectively extracts and recognizes the black insects from the realcolorimages with dark background compared with the ICCCM.

Color Edge Detector with Sobel-PCA

Edge detection is one of the most commonly used operations in computer vision, The key of edge detection is the choice of threshold; the choice of threshold directly determines the results of edge detection. How to automatically determine an optimal threshold is one of difficult points of edge detection. In this paper, Sobel edge detection operator and its improved algorithm are first discussed in term of optimal thresholding. We include color information into a color edge detection approach based on principal components analysis (PCA). Finally, the edge detection experiments of synthetic image and real color images are conducted by means of two algorithms. The comparative experiment results show that the new algorithm is very effective. The results are also better than the classical methods.

An ensemble-clustering-based distance metric and its applications

A distance metric learned from data reflects the actual similarity between objects better than the geometric distance. So, in this paper, we propose a new distance that is based on clustering. Because objects belonging to the same cluster usually share some common traits even though their geometric distance might be large. Thus, we perform several clustering runs to yield an ensemble of clustering results. The distance is defined by how many times the objects were not clustered together. To evaluate the ability of this new distance to reflect object similarity, we apply it to two types of data mining algorithms, classification kNN and selective sampling LSS. We experimented on standard numerical datasets and on real colour images. Using our distance, the algorithms run on equivalence classes instead of single objects, yielding a considerable speedup. We compared the kNN-EC classifier and LSS-EC algorithm to the original kNN and LSS algorithms.