Cartoon-texture Decomposition Research Articles

A variational model for cartoon-texture decomposition of a color image

Image decomposition is one of the most important tasks in image processing. In this paper, we develop a novel saturation-value total variation model for color image decomposition. The approach is to propose a variational model containing an energy functional to derive the cartoon and texture decomposition in the saturation-value color space so that the color can be kept in the resulting cartoon component. The proposed idea is different from existing color image decomposition model where color information usually exists in both cartoon and texture components. In the variational model, saturation-value total variation is incorporated for regularizing the cartoon component of a color image, meanwhile, L1 norm is considered for modeling the texture component in the saturation-value color space. Theoretically, we develop a smooth approximation approach to study the existence of the solution of the proposed optimization problem. We formulate an effective and efficient algorithm to solve the proposed minimization problem based on the framework of alternating direction method of multipliers. Numerical examples are presented to demonstrate that the performance of the proposed model is better than that of other testing methods for several testing color images.

Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture Decomposition

Low-Dose CT Denoising Algorithm Based on Image Cartoon Texture Decomposition

Image cartoon-texture decomposition by a generalized non-convex low-rank minimization method

Image cartoon-texture decomposition is an important problem in image processing. In recent years, by exploiting low-rank priors of images, low-rank minimization methods have been widely adopted for image cartoon-texture decomposition. Since matrix rank minimization is an NP-hard problem, the convex nuclear norm is often used as a substitute for the matrix’s rank to realize the low-rank minimization methods. In this paper, we utilize a generalized non-convex surrogate of the matrix rank function to develop a novel low-rank minimization model for image cartoon-texture decomposition. We design a proximal alternating algorithm to solve the non-convex model and further demonstrate the global convergence of the algorithm. Numerical experiments illustrate that the proposed method can show much better performances than the existing state-of-the-art methods for image cartoon-texture decomposition.

A coupling enhancement algorithm for ZrO2 ceramic bearing ball surface defect detection based on cartoon-texture decomposition model and multi-scale filtering method

To improve the detection accuracy of surface defects of ZrO2 ceramic bearing balls, a detection method for surface defects of ZrO2 ceramic bearing balls based on cartoon texture decomposition model is proposed. By building a surface defect detection system for ZrO2 ceramic bearing balls, the Gaussian curvature model is used to decompose the surface defect images of ZrO2 ceramic bearing balls, and the decomposed image layers are filtered by winner filtering and wavelet value domain filtering, and fused into clear and damage-free images The surface defect image of ZrO2 ceramic bearing ball and its detection. The experimental results shown the PSNR of image is 34.1 dB, the SSIM is 0.9476, the detection accuracy is 95.8 %, and the detection speed of a single defect image is 191 ms/img. The method can remove images noise, retain its details. It improves the efficiency and accuracy of ZrO2 ceramic bearing ball surface defect detection.

Learning Deep Blind Quality Assessment for Cartoon Images.

Although the cartoon industry has developed rapidly in recent years, few studies pay special attention to cartoon image quality assessment (IQA). Unfortunately, applying blind natural IQA algorithms directly to cartoons often leads to inconsistent results with subjective visual perception. Hence, this brief proposes a blind cartoon IQA method based on convolutional neural networks (CNNs). Note that training a robust CNN depends on manually labeled training sets. However, for a large number of cartoon images, it is very time-consuming and costly to manually generate enough mean opinion scores (MOSs). Therefore, this brief first proposes a full reference (FR) cartoon IQA metric based on cartoon-texture decomposition and then uses the estimated FR index to guide the no-reference IQA network. Moreover, in order to improve the robustness of the proposed network, a large-scale dataset is established in the training stage, and a stochastic degradation strategy is presented, which randomly implements different degradations with random parameters. Experimental results on both synthetic and real-world cartoon image datasets demonstrate the effectiveness and robustness of the proposed method.

An improved OSV cartoon-texture decomposition model

An improved OSV cartoon-texture decomposition model

Cartoon-Texture decomposition with patch-wise decorrelation

Cartoon-Texture decomposition (CTD) is a fundamental task and has wide applications in image processing and computer vision. To enhance separation of the cartoon and texture, existing models explicitly introduce correlation terms to decorrelate the two components. However, existing correlations usually ignore the local geometric structure information, thus insufficient to decorrelate cartoon and texture. In this work, we propose the patch-wise cosine similarity to decorrelate the cartoon and texture. The proposed decorrelation term takes the local geometric information into account and is more effective in separating cartoon and texture. Combining our decorrelation term with the regularities for cartoon (Relative Total Variation (RTV)) and texture (div(L1)-norm), we propose a new CTD model. Extended experiments show that the proposed model outperforms existing methods in CTD, especially in preserving edges of the cartoon.

Tri-modal medical image fusion based on adaptive energy choosing scheme and sparse representation

Multimodal medical image fusion integrates useful information from multiple single-modal medical images, generating a more comprehensive and objective fused image that better assist clinical applications. In this paper, a novel tri-modal medical image fusion method based on cartoon-texture decomposition is proposed and performed using a rolling guidance filter, and sparse representation, to fuse the texture components. Furthermore, a novel adaptive energy choosing scheme is proposed to fuse the cartoon components; through this approach, the brightness of cartoon components can be effectively detected. Finally, the fused image is reconstructed by combining the fused texture and cartoon components. Experimental results demonstrate that the proposed method yields better performance than some state-of-the-art methods in subjective and objective assessments. Meanwhile, the average level of the proposed method are 28.44%, 8.94%, 0.07%, 16.09%, 58.66%, and 0.34% higher than the compared methods evaluated by the metrics including QMI, QTE, QNCIE, QG, QP and EN, respectively.

A General Non-Lipschitz Infimal Convolution Regularized Model: Lower Bound Theory and Algorithm

The convex infimal convolution model proposed in Chambolle and Lions [Numer. Math., 76 (1997), pp. 167--188] is a fundamental model to extract two useful components from a single input image and has various low level vision applications. In many of them, one target component has an (approximately) piecewise constant structure and the other is a smoothly varying function or repeated texture pattern. In this paper, we propose and study a general non-Lipschitz infimal convolution (GnLIC) regularization model, which covers most existing applications in this type. Therein the non-Lipschitz regularization enforces the piecewise constant property of the first component. For this GnLIC model, we prove a lower bound theory for its local minimizers and a local version for its stationary points. Motivated by these, we naturally extend previous works to design an inexact iterative support shrinking algorithm with proximal linearization for our GnLIC model (InISSAPL-GnLIC). Moreover, we establish the sequence convergence property and a sequence lower bound theory for InISSAPL-GnLIC, provided that an inexact subgradient condition generated by a subsolver holds. The subsolver is constructed by efficient ADMM and a specially designed feasibilization operation. We finally give numerical experiments in two low level vision applications: Retinex and cartoon-texture decomposition. These tests demonstrate that our non-Lipschitz regularization based method can indeed extract the piecewise constant component better than existing approaches, which is consistent with the established lower bound theory.

Cartoon-texture evolution for two-region image segmentation

Two-region image segmentation is the process of dividing an image into two regions of interest, i.e., the foreground and the background. To this aim, Chan et al. (SIAM J Appl Math 66(5):1632–1648, 2006) designed a model well suited for smooth images. One drawback of this model is that it may produce a bad segmentation when the image contains oscillatory components. Based on a cartoon-texture decomposition of the image to be segmented, we propose a new model that is able to produce an accurate segmentation of images also containing noise or oscillatory information like texture. The novel model leads to a non-smooth constrained optimization problem which we solve by means of the ADMM method. The convergence of the numerical scheme is also proved. Several experiments on smooth, noisy, and textural images show the effectiveness of the proposed model.

An Artistic Image Fusion Method with Improved Cartoon-Texture Decomposition

When the art images are restored by the virtual restoration method, there are problems such as insufficient clarity and more noise in the reference image. An improved cartoon-texture decomposition method for art image fusion is proposed. The nonlinear local total variation component is used as the indicator function of image decomposition to obtain the image cartoon structure component and texture oscillation component. According to the oscillation component’s strong repetitiveness and structural directionality, the image texture part is filtered by combining the improved directional diffusion algorithm. Using the sparse coefficients of the fused cartoon component and the sparse coefficients of the texture component, the cartoon and texture of the image is inverse transformed and weighted and summed to obtain the recovered image after fusion. The experimental results show that this paper has a good effect after image fusion, and the recovered clarity is higher, which can better express the basic information of the source image; compared with several decomposition fusion methods commonly used at present, this paper has better recovery performance and detail processing ability and preserves the edge information of essential details in the image while filtering and denoising and is more excellent in objective performance evaluation indexes such as PSNR and SSIM. It can be used as a reference basis in the restoration process of art images.

Non-uniform illumination image enhancement for surface damage detection of wind turbine blades

Vision-based monitoring technology has been exploited for the surface damage detection of wind turbine blades (WTBs). However, the image quality is often significantly influenced by environmental illumination conditions, imposing difficulties for obtaining high detection accuracy for large-scale WTB surfaces. To improve the image quality and guarantee reliable damage detection on WTB surfaces, this study presents an image processing method for enhancing the images captured under non-uniform illumination conditions. First, cartoon and texture maps of the WTB images are constructed by cartoon texture decomposition. Second, an illumination model is established on the cartoon map from the Gaussian scale-space, to remove the non-uniform illumination. Third, the WTB images are enhanced by utilizing a multi-directional Gabor transformation to increase the contrast between the surface damage and image background. Finally, the WTB surface damages are detected using a gradient threshold segmentation method. The experimental results indicate that the damage detection accuracy of the WTB surfaces is significantly improved by using image enhancement. The F-measure and the intersection-over-union values of the damage detection are increased by 28.05% and 41.61%, respectively, relative to those detected from the input images. Therefore, this vision-based detection method for WTB surface damage inspection under non-uniform illumination has potential application values in practice.

Tri-Modal Medical Image Fusion Based on Cartoon-Texture Decomposition and Sparse Representation

Tri-Modal Medical Image Fusion Based on Cartoon-Texture Decomposition and Sparse Representation

A Novel Multi-Modality Image Simultaneous Denoising and Fusion Method Based on Sparse Representation

Multi-modality image fusion applied to improve image quality has drawn great attention from researchers in recent years. However, noise is actually generated in images captured by different types of imaging sensors, which can seriously affect the performance of multi-modality image fusion. As the fundamental method of noisy image fusion, source images are denoised first, and then the denoised images are fused. However, image denoising can decrease the sharpness of source images to affect the fusion performance. Additionally, denoising and fusion are processed in separate processing modes, which causes an increase in computation cost. To fuse noisy multi-modality image pairs accurately and efficiently, a multi-modality image simultaneous fusion and denoising method is proposed. In the proposed method, noisy source images are decomposed into cartoon and texture components. Cartoon-texture decomposition not only decomposes source images into detail and structure components for different image fusion schemes, but also isolates image noise from texture components. A Gaussian scale mixture (GSM) based sparse representation model is presented for the denoising and fusion of texture components. A spatial domain fusion rule is applied to cartoon components. The comparative experimental results confirm the proposed simultaneous image denoising and fusion method is superior to the state-of-the-art methods in terms of visual and quantitative evaluations.

Interference Stripe Noise Reduction of CMOS Sensor-Based Digital Holographic Measurement System

Comparing with the application of charge coupled device (CCD) sensor, a kind of interference stripe noise (ISN) can be observed easily from the complementary metal-oxide-semiconductor (CMOS) sensor-based digital holographic measurement (DHM) system because a series of optic coatings and filters are used in front of that sensor in order to improve its imaging effect. The improper assembly or the micro manufacturing error of these optic components may create the unexpected interference phenomena in the imaging sensor. To reduce that noise, an effective image processing method is developed. On one hand the Laplacian pyramid method is used to highlight the ISN and a cartoon-texture decomposition technique is carried out to remove the additive ISN. On the other hand, an improved Lee filtering is performed to restrain the multiplicative ISN. Extensive experiment results have shown that the image quality can be improved after the ISN reduction and this method is fit for solving a deionized water temperature estimation application where the texture complexity of target image is relatively low.

CSID: A Novel Multimodal Image Fusion Algorithm for Enhanced Clinical Diagnosis.

Technology-assisted clinical diagnosis has gained tremendous importance in modern day healthcare systems. To this end, multimodal medical image fusion has gained great attention from the research community. There are several fusion algorithms that merge Computed Tomography (CT) and Magnetic Resonance Images (MRI) to extract detailed information, which is used to enhance clinical diagnosis. However, these algorithms exhibit several limitations, such as blurred edges during decomposition, excessive information loss that gives rise to false structural artifacts, and high spatial distortion due to inadequate contrast. To resolve these issues, this paper proposes a novel algorithm, namely Convolutional Sparse Image Decomposition (CSID), that fuses CT and MR images. CSID uses contrast stretching and the spatial gradient method to identify edges in source images and employs cartoon-texture decomposition, which creates an overcomplete dictionary. Moreover, this work proposes a modified convolutional sparse coding method and employs improved decision maps and the fusion rule to obtain the final fused image. Simulation results using six datasets of multimodal images demonstrate that CSID achieves superior performance, in terms of visual quality and enriched information extraction, in comparison with eminent image fusion algorithms.

Spatially Adaptive Regularization in Image Segmentation

We present a total-variation-regularized image segmentation model that uses local regularization parameters to take into account spatial image information. We propose some techniques for defining those parameters, based on the cartoon-texture decomposition of the given image, on the mean and median filters, and on a thresholding technique, with the aim of preventing excessive regularization in piecewise-constant or smooth regions and preserving spatial features in nonsmooth regions. Our model is obtained by modifying a well-known image segmentation model that was developed by T. Chan, S. Esedoḡlu, and M. Nikolova. We solve the modified model by an alternating minimization method using split Bregman iterations. Numerical experiments show the effectiveness of our approach.

A multi-modal image fusion framework based on guided filter and sparse representation

To completely integrate the structures of images, a cartoon-texture decomposition based image fusion framework is proposed in this paper. The cartoon components represent the background and main structures information. Due to its high similarity, the sparse representation is utilized based on an overcomplete cartoon dictionary. Moreover, a modified orthogonal matching pursuit is proposed to improve the fusion image continuity and eliminate artifacts interferences. The texture components describe more distinct structures, which have diverse scale characteristics. To preserve more relevant infrared details in fused texture, an iterative visual saliency map is proposed. Furthermore, the weight maps are computed on the basis of a designed multi-scale guided filter and saliency map; then, the weight maps are utilized to guide the texture fusion, aiming to improve the visual and scale-aware salience. The final fused image is obtained by combining the cartoon fusion and texture fusion results. The comprehensive and objection evaluations demonstrate the outperformance on accuracy, robustness and versatility of the proposed fusion framework.

An Image Decomposition Fusion Method for Medical Images

A fusion method based on the cartoon+texture decomposition method and convolution sparse representation theory is proposed for medical images. It can be divided into three steps: firstly, the cartoon and texture parts are obtained using the improved cartoon-texture decomposition method. Secondly, the fusion rules of energy protection and feature extraction are used in the cartoon part, while the fusion method of convolution sparse representation is used in the texture part. Finally, the fused image is obtained using superimposing the fused cartoon and texture parts. Experiments show that the proposed algorithm is effective.

An L0 regularized cartoon-texture decomposition model for restoring images corrupted by blur and impulse noise

In this paper, we address the image restoration case that includes both blurring and impulse noise. To recover an image with abundant features, we propose an L0 regularized cartoon-texture model for the simultaneous deblurring and impulse noise removal problem. We propose an L0 regularized framelet-based sparse representation and L0 regularized discrete cosine transform (DCT)-based sparse approximation to model the cartoon and texture of images, respectively. Unlike other cartoon-texture decomposition based-restoration approaches, our method does not depend on local features but globally controls the important non-zero components of the cartoon and texture in the framelet and DCT domain. Furthermore, we develop an alternating half-quadratic splitting method to solve the proposed L0 regularized cartoon-texture deblurring and impulse noise removal model (L0_RCTDINR) by introducing an alternating algorithm into the half-quadratic method. Experiments show the effectiveness of L0_RCTDINR on deblurring and impulse noise removal compared with existing state-of-the-art methods.