Dual-tree Complex Wavelet Transform Domain Research Articles

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance. MATLAB software was used for simulation of the proposed method. In comparison with the particle swarm optimized-DTCWT (PSODTCWT) and DTCWT algorithms, respectively, the results demonstrated an improvement in the peak signal-to-noise ratio of 8.92% and 15.92% and a higher structural similarity index measure of 3.69% and 7.5%. Based on these improvements, the proposed method could potentially make high-quality, real-time MRI imaging possible, which might improve detection and treatment of medical conditions and increase the throughput of MRI machines.

Blind Camcording-Resistant Video Watermarking in the DTCWT and SVD Domain

Video watermarking techniques can be used to prevent unauthorized users from illegally distributing videos across (social) media networks. However, current watermarking solutions are unable to embed a perceptually invisible watermark which is robust to the distortions introduced by camcording. These watermark-disrupting distortions include lossy compression, the addition of noise, frame-rate conversion and geometric distortions. In this paper, we present a novel video watermarking technique that is blind and robust to camcording attacks. The proposed approach uses the integration of the dual-tree complex wavelet transform (DTCWT) and singular value decomposition (SVD) to achieve robustness against geometric attacks. The experimental results validate our technique’s superior imperceptibility and robustness to several attacks when compared to existing peer mechanisms. In conclusion, the proposed technique can be used to protect against illegal distribution of video content.

Image Reconstruction from Multiscale Singular Points Based on the Dual-Tree Complex Wavelet Transform

The representation of an image with several multiscale singular points has been the main concern in image processing. Based on the dual-tree complex wavelet transform (DT-CWT), a new image reconstruction (IR) algorithm from multiscale singular points is proposed. First, the image was transformed by DT-CWT, which provided multiresolution wavelet analysis. Then, accurate multiscale singular points for IR were detected in the DT-CWT domain due to the shift invariance and directional selectivity properties of DT-CWT. Finally, the images were reconstructed from the phases and magnitudes of the multiscale singular points by alternating orthogonal projections between the CT-DWT space and its affine space. Theoretical analysis and experimental results show that the proposed IR algorithm is feasible, efficient, and offers a certain degree of denoising. Furthermore, the proposed IR algorithm outperforms other classical IR algorithms in terms of performance metrics such as peak signal-to-noise ratio, mean squared error, and structural similarity.

Infrared and Visible Image Fusion Based on Sparse Representation and Spatial Frequency in DTCWT Domain

Infrared and visible image fusion is a key area of research in multi-sensor image fusion. The main purpose of this fusion is to combine thermal information of the infrared image and texture information of the visible image. This paper presents an image fusion framework, based on parallel arrangement of sparse representation (SR) and spatial frequency (SF). In the proposed framework, an efficient edge-aware filter, i.e. guided filter, is first employed on the visible image. Then dual-tree complex wavelet transform (DTCWT) is used to obtain low-pass and high-pass coefficients of images, as it is shift-invariant and has high directional selectivity. The low-pass coefficients are fused using the SR- and SF-based fusion rules in parallel, which enhances the regional features of the images. The simulation results show that the proposed technique has better performance when compared with conventional techniques in both subjective and objective evaluations.

Combining Statistical Features and Local Pattern Features for Texture Image Retrieval

The complementary fusion of global and local features can effectively improve the performance of image retrieval. This article proposes a new local texture descriptor, combined with statistical modeling in transform domain for texture image retrieval. The proposed local descriptor calculates the eight directions of the central pixel by using the relationship between the central pixel and the neighboring pixels in six directions, which is called the local eight direction pattern (LEDP). In the texture image retrieval system of this article, the feature extraction part combines global statistical features and local pattern features. Among them, both the relative magnitude (RM) sub-band coefficients and relative phase (RP) sub-band coefficients are modeled as wrapped Cauchy (WC) distribution in the dual-tree complex wavelet transform (DTCWT) domain, and the global statistical features employ the parameters of this model; while the local pattern features respectively choose the local binary pattern (LBP) histogram features in the spatial domain and the LEDP histogram features of each direction sub-band in the DTCWT domain. On the other hand, the similarity measurement selects matching distances for different features and combines them in the form of convex linear optimization. Texture image retrieval experiments are conducted in the Corel-1k database (DB1), Brodatz texture database (DB2) and MIT VisTex texture database (DB3), respectively. Experimental results show that, compared with the best existing methods, the approach proposed in this article has achieved better retrieval performance.

Robust additive watermarking in the DTCWT domain based on perceptual masking

In this paper, a robust additive image watermarking system operating in the Dual Tree Complex Wavelet Transform (DTCWT) domain is proposed. The system takes advantage of a new perceptual masking model that exploits the Human Visual System (HVS) characteristics at the embedding stage. It also uses an efficient watermark detection structure, called the Rao-test, to verify the presence of the candidate watermark. This structure relies on the statistical modeling of high frequency DTCWT coefficients by the Generalized Gaussian distribution. Experimental results show that the proposed system outperforms related state-of-the-art watermarking systems in terms of imperceptibility and robustness.

Fusing DTCWT and LBP Based Features for Rotation, Illumination and Scale Invariant Texture Classification

Classification of texture images with different orientation, illumination, and scale changes is a challenging problem in computer vision and pattern recognition. This paper proposes two descriptors and uses them jointly to fulfill such task. One can obtain an image pyramid by applying dual-tree complex wavelet transform (DTCWT) on the original image, and generate local binary patterns (LBP) in DTCWT domain, called LBPDTCWT, as local texture features. Moreover, log-polar (LP) transform is applied on the original image, and the energies of DTCWT coefficients on detail subbands of the LP image, called LPDTCWTE, are taken as global texture features. We fuse the two kinds of features for texture classification, and the experimental results on benchmark data sets show that our proposed method can achieve better performance than other the state-of-the-art methods.

Complex Wavelet-Domain Image Watermarking Algorithm Using $$L_1$$ L 1 -Norm Function-Based Quantization

Digital watermarking is an efficient and promising means to protect intellectual properties. Based on growing attention on the intellectual property, protection of the product ownership becomes extremely important and necessary. In this paper, a quantization-based image watermarking method is proposed using dual-tree complex wavelet transform (DT-CWT) and the \(L_1\)-norm function. To enhance the robustness, the watermark is embedded in the DT-CWT domain of the high-frequency sub-band images by using the \(L_1\)-norm function. The performance of the proposed scheme is investigated in terms of image quality (peak signal-to-noise ratio or PSNR) and robustness (bit error ratio or BER). Experimental results show that the proposed method is robust against noise attack, filtering, JPEG compression, amplitude scaling and gain attack with a good invisibility.

Multifocus Image Fusion in Q-Shift DTCWT Domain Using Various Fusion Rules

Multifocus image fusion is a process that integrates partially focused image sequence into a fused image which is focused everywhere, with multiple methods proposed in the past decades. The Dual Tree Complex Wavelet Transform (DTCWT) is one of the most precise ones eliminating two main defects caused by the Discrete Wavelet Transform (DWT). Q-shift DTCWT was proposed afterwards to simplify the construction of filters in DTCWT, producing better fusion effects. A different image fusion strategy based on Q-shift DTCWT is presented in this work. According to the strategy, firstly, each image is decomposed into low and high frequency coefficients, which are, respectively, fused by using different rules, and then various fusion rules are innovatively combined in Q-shift DTCWT, such as the Neighborhood Variant Maximum Selectivity (NVMS) and the Sum Modified Laplacian (SML). Finally, the fused coefficients could be well extracted from the source images and reconstructed to produce one fully focused image. This strategy is verified visually and quantitatively with several existing fusion methods based on a plenty of experiments and yields good results both on standard images and on microscopic images. Hence, we can draw the conclusion that the rule of NVMS is better than others after Q-shift DTCWT.

Adaptive-weighted bilateral filtering and other pre-processing techniques for optical coherence tomography

This paper presents novel pre-processing image enhancement algorithms for retinal optical coherence tomography (OCT). These images contain a large amount of speckle causing them to be grainy and of very low contrast. To make these images valuable for clinical interpretation, we propose a novel method to remove speckle, while preserving useful information contained in each retinal layer. The process starts with multi-scale despeckling based on a dual-tree complex wavelet transform (DT-CWT). We further enhance the OCT image through a smoothing process that uses a novel adaptive-weighted bilateral filter (AWBF). This offers the desirable property of preserving texture within the OCT image layers. The enhanced OCT image is then segmented to extract inner retinal layers that contain useful information for eye research. Our layer segmentation technique is also performed in the DT-CWT domain. Finally we describe an OCT/fundus image registration algorithm which is helpful when two modalities are used together for diagnosis and for information fusion.

A semi-fragile watermarking scheme for authenticating audio signal based on dual-tree complex wavelet transform and discrete cosine transform

In this paper, a novel semi-fragile watermarking scheme for authenticating an audio signal based on dual-tree complex wavelet transform (DT-CWT) and discrete cosine transform (DCT) is proposed. Specifically, the watermark data are efficiently inserted into the coefficients of the low-frequency sub-band of DT-CWT taking advantages of both DCT and quantization index modulation (QIM). First, the original digital audio signal is segmented and then performed with DT-CWT. Second, based on the energy compression property, the low-frequency sub-band coefficients of the DT-CWT domain are performed with DCT, and the DC component is utilized to embed one distorted watermark bit by the QIM technique. Finally, inverse DCT and DT-CWT are orderly implemented on the watermarked coefficients of each audio segment to get a watermarked audio signal. Simulation results show that the hybrid embedding domain constructed by DT-CWT and DCT is effective, and the proposed watermarking scheme is not only inaudible, but also robust against content persistent non-malicious audio signal processing operations, such as MP3 compression, noise addition, re-sampling, re-quantization, etc. Furthermore, the proposed scheme can effectively authenticate the veracity and integrity of audio content and greatly expands the applicability of the audio watermarking scheme.

Effective bias removal for fringe projection profilometry using the dual-tree complex wavelet transform

When reconstructing the three-dimensional (3D) object height profile using the fringe projection profilometry (FPP) technique, the light intensity reflected from the object surface can yield abruptly changing bias in the captured fringe image, which leads to severe reconstruction error. The traditional approach tries to remove the bias by suppressing the zero spectrum of the fringe image. It is based on the assumption that the aliasing between the frequency spectrum of the bias, which is around the zero frequency, and the frequency spectrum of the fringe is negligible. This, however, is not the case in practice. In this paper, we propose a novel (to our knowledge) technique to eliminate the bias in the fringe image using the dual-tree complex wavelet transform (DT-CWT). The new approach successfully identifies the features of bias, fringe, and noise in the DT-CWT domain, which allows the bias to be effectively extracted from a noisy fringe image. Experimental results show that the proposed algorithm is superior to the traditional methods and facilitates accurate reconstruction of objects' 3D models.

Image denoising with anisotropic bivariate shrinkage

This paper presents a novel image denoising algorithm based on the modeling of wavelet coefficients with an anisotropic bivariate Laplacian distribution function. The anisotropic bivariate Laplacian model not only captures the child–parent dependency between wavelet coefficients, but also fits the anisotropic property of the variances of wavelet coefficients in different scales of natural images. With this statistical model, we derive a closed-form anisotropic bivariate shrinkage function in the framework of Bayesian denoising and a new image denoising approach with local marginal variance estimation based on this newly derived shrinkage function is proposed in the discrete wavelet transform (DWT) domain. The proposed anisotropic bivariate shrinkage approach is also extended to the dual-tree complex wavelet transform (DT-CWT) domain to further improve the performance of image denoising. To take full advantage of DT-CWT, a more accurate noise variance estimator is proposed and the way the anisotropic bivariate shrinkage function applied to the magnitudes of DT-CWT coefficients is presented. Experiments were carried out in both the DWT and the DT-CWT domain to validate the effectiveness of the proposed method. Using a representative set of standard test images corrupted by additive white Gaussian noise, the simulation results show that the proposed method provides promising results and is competitive with the best wavelet-based denoising results reported in the literature both in terms of peak signal-to-noise ratio (PSNR) and in visual quality.

Blind Detection of Additive Spread-Spectrum Watermarking in the Dual-Tree Complex Wavelet Transform Domain

In this paper, the authors adapt two blind detector structures for additive spread-spectrum image watermarking to the host signal characteristics of the Dual-Tree Complex Wavelet Transform (DT-CWT) domain coefficients. The research is motivated by the superior perceptual characteristics of the DT-CWT and its active use in watermarking. To improve the numerous existing watermarking schemes in which the host signal is modeled by a Gaussian distribution, the authors show that the Generalized Gaussian nature of Dual-Tree detail subband statistics can be exploited for better detector performance. This paper finds that the Rao detector is more practical than the likelihood-ratio test for their detection problem. The authors experimentally investigate the robustness of the proposed detectors under JPEG and JPEG2000 attacks and assess the perceptual quality of the watermarked images. The results demonstrate that their alterations allow significantly better blind watermark detection performance in the DT-CWT domain than the widely used linear-correlation detector. As only the detection side has to be modified, the proposed methods can be easily adopted in existing DT-CWT watermarking schemes.

方向性差分を用いたFINDRMと分解係数間の関係を考慮した双対木複素ウェーブレットを用いた混合雑音除去

方向性差分を用いたFINDRMと分解係数間の関係を考慮した双対木複素ウェーブレットを用いた混合雑音除去