Showing papers on "Contourlet published in 2016"

Multimodal Sensor Medical Image Fusion Based on Type-2 Fuzzy Logic in NSCT Domain

Abstract: Multimodal medical image fusion plays a vital role in different clinical imaging sensor applications. This paper presents a novel multimodal medical image fusion method that adopts a multiscale geometric analysis of the nonsubsampled contourlet transform (NSCT) with type-2 fuzzy logic techniques. First, the NSCT was performed on preregistered source images to obtain their high- and low-frequency subbands. Next, an effective type-2 fuzzy logic-based fused rule is proposed for fusion of the high-frequency subbands. In the presented fusion approach, the local type-2 fuzzy entropy is introduced to automatically select high-frequency coefficients. However, for the low-frequency subbands, they were fused by a local energy algorithm based on the corresponding image’s local features. Finally, the fused image was constructed by the inverse NSCT with all composite subbands. Both subjective and objective evaluations showed better contrast, accuracy, and versatility in the proposed approach compared with state-of-the-art methods. Besides, an effective color medical image fusion scheme is also given in this paper that can inhibit color distortion to a large extent and produce an improved visual effect.

ShearLab 3D: Faithful Digital Shearlet Transforms Based on Compactly Supported Shearlets

Abstract: Wavelets and their associated transforms are highly efficient when approximating and analyzing one-dimensional signals. However, multivariate signals such as images or videos typically exhibit curvilinear singularities, which wavelets are provably deficient in sparsely approximating and also in analyzing in the sense of, for instance, detecting their direction. Shearlets are a directional representation system extending the wavelet framework, which overcomes those deficiencies. Similar to wavelets, shearlets allow a faithful implementation and fast associated transforms. In this article, we will introduce a comprehensive carefully documented software package coined ShearLab 3D (www.ShearLab.org) and discuss its algorithmic details. This package provides MATLAB code for a novel faithful algorithmic realization of the 2D and 3D shearlet transform (and their inverses) associated with compactly supported universal shearlet systems incorporating the option of using CUDA. We will present extensive numerical experiments in 2D and 3D concerning denoising, inpainting, and feature extraction, comparing the performance of ShearLab 3D with similar transform-based algorithms such as curvelets, contourlets, or surfacelets. In the spirit of reproducible research, all scripts are accessible on www.ShearLab.org.

Spatial Methods for Multispectral Pansharpening: Multiresolution Analysis Demystified

Abstract: The majority of multispectral (MS) pansharpening methods may be labeled as spectral or spatial, depending on whether the geometric details that shall be injected into the interpolated MS bands are extracted from the panchromatic (P) image by means of a spectral transformation of MS pixels or a spatial transformation of the P image, achieved by means of linear shift-invariant digital filters. Spectral methods are known as component substitution; spatial methods are based on multiresolution analysis (MRA). In this paper, the authors show that, under the most general conditions, MRA-based pansharpening is characterized by a unique separable low-pass filter, which can be parametrically optimized based on the modulation transfer function (MTF) of the MS instrument, possibly followed by decimation and interpolation stages. This happens for the discrete wavelet transform (DWT) and its undecimated version (UDWT), for the “a-trous” wavelet (ATW) transform and its decimated version, i.e., the generalized Laplacian pyramid (GLP), and for nonseparable wavelet transforms, such as the nonsubsampled contourlet transform (NSCT). Hybrid methods, in which MRA fusion is performed on the intensity component derived from a spectral transformation, are equivalent to MRA fusion with a specific detail injection model. ATW and GLP are preferable to DWT, UDWT, and NSCT, because of computational benefits and of a looser choice of the low-pass filter, unconstrained from the requirement of generating a perfect reconstruction filter bank. Ultimately, GLP outperforms ATW, because its decimation and interpolation stages allow the aliasing impairments intrinsically present in the original MS bands to be removed from the pansharpened product.

Multiplicative Watermark Decoder in Contourlet Domain Using the Normal Inverse Gaussian Distribution

Abstract: In recent years, many works on digital image watermarking have been proposed all aiming at protection of the copyright of an image document or authentication of data. This paper proposes a novel watermark decoder in the contourlet domain . It is known that the contourlet coefficients of an image are highly non-Gaussian and a proper distribution to model the statistics of the contourlet coefficients is a heavy-tailed PDF. It has been shown in the literature that the normal inverse Gaussian (NIG) distribution can suitably fit the empirical distribution. In view of this, statistical methods for watermark extraction are proposed by exploiting the NIG as a prior for the contourlet coefficients of images. The proposed watermark extraction approach is developed using the maximum likelihood method based on the NIG distribution. Closed-form expressions are obtained for extracting the watermark bits in both clean and noisy environments. Experiments are performed to verify the robustness of the proposed decoder. The results show that the proposed decoder is superior to other decoders in terms of providing a lower bit error rate. It is also shown that the proposed decoder is highly robust against various kinds of attacks such as noise, rotation, cropping, filtering, and compression.

A robust color video watermarking scheme based on hybrid embedding techniques

Abstract: The advancements in network technologies and processing of multimedia contents have provided the way for the distribution and sharing of multimedia contents through networks. This in turn has increased the demand for protecting the multimedia contents in terms of authentication, proof of ownership, copy control etc., which can be achieved by means of what is called digital watermarking. The challenges in watermarking techniques are how to achieve the imperceptibility, robustness and payload simultaneously. This paper presents a new bit plane sliced, scrambled color image watermark embedded on the color cover video using hybrid transforms such as Contourlet Transform (CT), Discrete Wavelet Transform (DWT) and Singular Value Decomposition (SVD) transformations with good imperceptibility, high robustness and at an information rate of (N ź number of motion frames) / 24 images per second of the video, where N is the total number of frames in the video. In order to achieve a good level of imperceptibility, we perform the following: First, we slice the color watermark image into 24 slices using the bit plane slicing mechanism. Subsequently, the so called Arnold transformation key is used to scramble those slices, to achieve first-level of security. Thus, an authenticated receiver with an appropriate key alone can descramble the received slices. Second, we embed those scrambled slices on one of the DWT mid-frequency coefficients (LH band) of successive 1-level CT non-motion frames of color cover video. The non-motion frames are identified using the histogram difference based shot boundary detection algorithm. Third, in order to the provide second-level of security, we generate a random eigen vector from the color watermark image, using co-variance matrix and maximum eigen value and then embed it on another DWT mid-frequency coefficients (HL band). Thus, embedding only the slices (not an entire image) will improve the level of imperceptibility. The mid-frequency embedding location can withstand against all low pass and high pass filtering attacks; thereby it increases the level of robustness. Thus, the proposed system is suitable for authentication. Finally, as far as payload is concerned, we need only 24 non-motion frames for embedding our watermark on to the cover video. Hence the remaining frames can be utilized for embedding other color images. Our simulation results prove that the proposed system provides trustworthy performance against various notable image processing attacks, multiple attacks, geometrical attacks, and temporal attacks.

Scheme for unsupervised colour–texture image segmentation using neutrosophic set and non-subsampled contourlet transform

Abstract: The process of partitioning an image into some different meaningful regions with the homogeneous characteristics is called the image segmentation which is a crucial task in image analysis. This study presents an efficient scheme for unsupervised colour–texture image segmentation using neutrosophic set (NS) and non-subsampled contourlet transform (NSCT). First, the image colour and texture information are extracted via CIE Luv colour space model and NSCT, respectively. Then, the extracted colour and texture information are transformed into the NS domain efficiently by the authors’ proposed approach. In the NS-based image segmentation, the indeterminacy assessment of the images in the NS domain is notified by the entropy concept. The lower quantity of indeterminacy in the NS domain, the higher confidence and easier segmentation could be achieved. Therefore, to achieve a better segmentation result, an appropriate indeterminacy reduction operation is proposed. Finally, the K-means clustering algorithm is applied to perform the image segmentation in which the cluster number K is determined by the cluster validity analysis. To show the effectiveness of their proposed method, its performance is compared with that of the state-of-the-art methods. The experimental results reveal that their segmentation scheme outperforms the other methods for the Berkeley dataset.

Laplacian Patch-Based Image Synthesis

Abstract: Patch-based image synthesis has been enriched with global optimization on the image pyramid. Successively, the gradient-based synthesis has improved structural coherence and details. However, the gradient operator is directional and inconsistent and requires computing multiple operators. It also introduces a significantly heavy computational burden to solve the Poisson equation that often accompanies artifacts in non-integrable gradient fields. In this paper, we propose a patch-based synthesis using a Laplacian pyramid to improve searching correspondence with enhanced awareness of edge structures. Contrary to the gradient operators, the Laplacian pyramid has the advantage of being isotropic in detecting changes to provide more consistent performance in decomposing the base structure and the detailed localization. Furthermore, it does not require heavy computation as it employs approximation by the differences of Gaussians. We examine the potentials of the Laplacian pyramid for enhanced edge-aware correspondence search. We demonstrate the effectiveness of the Laplacian-based approach over the state-of-the-art patchbased image synthesis methods.

NSCT-Based Infrared Image Enhancement Method for Rotating Machinery Fault Diagnosis

Abstract: Infrared images are usually subject to low contrast, edge blurring, and high noise. Especially for machinery diagnosis, the range of temperature variation is narrow, which causes the difficulty to diagnose different equipment conditions directly from infrared images. To enhance fault-related information extraction and improve diagnosis accuracy, a new infrared image analysis method based on nonsubsampled contourlet transform is investigated with fuzzy enhancement and nonlinear gain. The parameters of fuzzy enhancement and nonlinear gain functions are optimized by particle swarming optimization algorithm, in which the optimization criterion is formulated by information entropy and contrast of infrared image. Feature extraction and dimensionality reduction methods are then applied to select features for further diagnosis. The effectiveness of the presented method is experimentally validated in the infrared image analysis of rotor test stand in laboratory, and the results show that the presented method can effectively enhance the fault information, enlarge the contrast of image under different conditions, and improve the accuracy of machinery fault diagnosis.

Multimodal Medical Image Fusion in Non-Subsampled Contourlet Transform Domain

Abstract: Multimodal medical image fusion is a powerful tool for diagnosing diseases in medical field. The main objective is to capture the relevant information from input images into a single output image, which plays an important role in clinical applications. In this paper, an image fusion technique for the fusion of multimodal medical images is proposed based on Non-Subsampled Contourlet Transform. The proposed technique uses the Non-Subsampled Contourlet Transform (NSCT) to decompose the images into lowpass and highpass subbands. The lowpass and highpass subbands are fused by using mean based and variance based fusion rules. The reconstructed image is obtained by taking Inverse Non-Subsampled Contourlet Transform (INSCT) on fused subbands. The experimental results on six pairs of medical images are compared in terms of entropy, mean, standard deviation, QAB/F as performance parameters. It reveals that the proposed image fusion technique outperforms the existing image fusion techniques in terms of quantitative and qualitative outcomes of the images. The percentage improvement in entropy is 0% - 40%, mean is 3% - 42%, standard deviation is 1% - 42%, QAB/Fis 0.4% - 48% in proposed method comparing to conventional methods for six pairs of medical images.

PolSAR Image Classification via D-KSVD and NSCT-Domain Features Extraction

Abstract: Polarimetric synthetic aperture radar (PolSAR) image classification is a powerful and important application in remote sensing. In this letter, we propose a PolSAR image classification method based on discriminative dictionary learning (D-KSVD) model and nonsubsampled contourlet transform (NSCT)-domain features. The D-KSVD model is used in our experiment to classify PolSAR images and is more time efficient and accurate when compared with the sparse representation classifier (SRC) used by other researchers to the PolSAR image classification. This is due to the fact that the D-KSVD model employs linear classifier rather than compute the reconstruction error employed by SRC. On constructing an effective dictionary of D-KSVD, we use NSCT to extract polarization features from the PolSAR coherency matrix. However, the low-frequency coefficients of NSCT domain have better discrimination ability than others, and we consider them as the classification features; hence, we adopt them into the D-KSVD model to obtain a dictionary which accommodates a linear classifier. Our experimental results of two real PolSAR images indicate that the proposed PolSAR image classification method based on D-KSVD model and NSCT-domain features extraction approach achieves better results with time efficiency and accuracy.

Multisource Remote Sensing Imagery Fusion Scheme Based on Bidimensional Empirical Mode Decomposition (BEMD) and Its Application to the Extraction of Bamboo Forest

Abstract: Most bamboo forests grow in humid climates in low-latitude tropical or subtropical monsoon areas, and they are generally located in hilly areas. Bamboo trunks are very straight and smooth, which means that bamboo forests have low structural diversity. These features are beneficial to synthetic aperture radar (SAR) microwave penetration and they provide special information in SAR imagery. However, some factors (e.g., foreshortening) can compromise the interpretation of SAR imagery. The fusion of SAR and optical imagery is considered an effective method with which to obtain information on ground objects. However, most relevant research has been based on two types of remote sensing image. This paper proposes a new fusion scheme, which combines three types of image simultaneously, based on two fusion methods: bidimensional empirical mode decomposition (BEMD) and the Gram-Schmidt transform. The fusion of panchromatic and multispectral images based on the Gram-Schmidt transform can enhance spatial resolution while retaining multispectral information. BEMD is an adaptive decomposition method that has been applied widely in the analysis of nonlinear signals and to the nonstable signal of SAR. The fusion of SAR imagery with fused panchromatic and multispectral imagery using BEMD is based on the frequency information of the images. It was established that the proposed fusion scheme is an effective remote sensing image interpretation method, and that the value of entropy and the spatial frequency of the fused images were improved in comparison with other techniques such as the discrete wavelet, a-trous, and non-subsampled contourlet transform methods. Compared with the original image, information entropy of the fusion image based on BEMD improves about 0.13–0.38. Compared with the other three methods it improves about 0.06–0.12. The average gradient of BEMD is 4%–6% greater than for other methods. BEMD maintains spatial frequency 3.2–4.0 higher than other methods. The experimental results showed the proposed fusion scheme could improve the accuracy of bamboo forest classification. Accuracy increased by 12.1%, and inaccuracy was reduced by 11.0%.

Multimodal image fusion via sparse representation and clustering-based dictionary learning algorithm in NonSubsampled Contourlet domain

Abstract: Image fusion is a widely used technique for enhancing the interpretation quality of images in medical application, which use different medical imaging sensors. This paper presents an image fusion framework for images acquired from two distinct medical imaging sensor modalities (i.e. PET and MRI) based on sparse representation in Non Sub-sampled Contourlet transform (NSCT) domain. NSCT firstly performed on pre-registered source images to obtain their low-pass and high-pass sub-bands. Then, low-pass sub-bands are fused by sparse representation based approach, using a clustering-based dictionary learning while high-pass sub-bands are merged using salience match measure rule. Constructing a compact and informative dictionary is an important step toward a successful image fusion technique in sparsity based models. This paper presents efficient dictionary learning method by clustering patches of several directional sub-bands of different source image in NSCT domain. The experimental results demonstrate effectiveness of the proposed dictionary learning algorithm and priority of general framework, in terms of both subjective and objective evaluation for image fusion task.

Performance evaluation of wavelet, ridgelet, curvelet and contourlet transforms based techniques for digital image denoising

Abstract: Digital images always inherit some extent of noise in them. This noise affects the information content of the image. Removal of this noise is very important to extract useful information from an image. However noise cannot be eliminated, it can only be minimized due to overlap between the signal and noise characteristics. This paper reviews image denoising algorithms which are based on wavelet, ridgelet, curvelet and contourlet transforms and benchmarks them based on the published results. This article presents the techniques, parameters used for benchmarking, denoising performance on standard images and a comparative analysis of the same. This paper highlights various trends in denoising techniques, based on which it has been concluded that a single parameter Peak Signal to Noise Ratio (PSNR) cannot exactly represent the denoising performance until other parameters are consistent. A new robust parameter Performance measure `P' is presented as a measure of denoising performance on the basis of a new concept named Noise Improvement Rectangle followed by its analysis. The results of the published algorithms are presented in tabular format in terms of PSNR and P which facilitates readers to have a bird's eye view of the research work in the field of image denoising and restoration.