Showing papers on "Contourlet published in 2014"
TL;DR: Experimental results demonstrate that the proposed hybrid multifocus image fusion method is better than various existing transform-based fusion methods, including gradient pyramid transform, discrete wavelet transform, NSCT, and a spatial-based method, in terms of both subjective and objective evaluations.
Abstract: To overcome the difficulties of sub-band coefficients selection in multiscale transform domain-based image fusion and solve the problem of block effects suffered by spatial domain-based image fusion, this paper presents a novel hybrid multifocus image fusion method. First, the source multifocus images are decomposed using the nonsubsampled contourlet transform (NSCT). The low-frequency sub-band coefficients are fused by the sum-modified-Laplacian-based local visual contrast, whereas the high-frequency sub-band coefficients are fused by the local Log-Gabor energy. The initial fused image is subsequently reconstructed based on the inverse NSCT with the fused coefficients. Second, after analyzing the similarity between the previous fused image and the source images, the initial focus area detection map is obtained, which is used for achieving the decision map obtained by employing a mathematical morphology postprocessing technique. Finally, based on the decision map, the final fused image is obtained by selecting the pixels in the focus areas and retaining the pixels in the focus region boundary as their corresponding pixels in the initial fused image. Experimental results demonstrate that the proposed method is better than various existing transform-based fusion methods, including gradient pyramid transform, discrete wavelet transform, NSCT, and a spatial-based method, in terms of both subjective and objective evaluations.
167 citations
TL;DR: A novel unsupervised change detection method in SAR images based on image fusion strategy and compressed projection is presented, which is effective for SAR image change detection in terms of shape preservation of the detected change portion and the numerical results.
Abstract: Multitemporal synthetic aperture radar (SAR) images have been successfully used for the detection of different types of terrain changes. SAR image change detection has recently become a challenge problem due to the existence of speckle and the complex mixture of terrain environment. This paper presents a novel unsupervised change detection method in SAR images based on image fusion strategy and compressed projection. First, a Gauss-log ratio operator is proposed to generate a difference image. In order to obtain a better difference map, image fusion strategy is applied using complementary information from Gauss-log ratio and log-ratio difference image. Second, nonsubsampled contourlet transform (NSCT) is used to reduce the noise of the fused difference image, and compressed projection is employed to extract feature for each pixel. The final change detection map is obtained by partitioning the feature vectors into “changed” and “unchanged” classes using simple k-means clustering. Experiment results show that the proposed method is effective for SAR image change detection in terms of shape preservation of the detected change portion and the numerical results.
132 citations
TL;DR: The experimental and comparison results show that luminance and contrast is of great importance for image processing and prove that the proposed method is better than all other methods.
107 citations
TL;DR: A survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects and the existing algorithms in each subgroup appear to be limited for detecting some subgroup of defects.
Abstract: Ceramic and tile industries should indispensably include a grading stage to quantify the quality of products. Actually, human control systems are often used for grading purposes. An automatic grading system is essential to enhance the quality control and marketing of the products. Since there generally exist six different types of defects originating from various stages of tile manufacturing lines with distinct textures and morphologies, many image processing techniques have been proposed for defect detection. In this paper, a survey has been made on the pattern recognition and image processing algorithms which have been used to detect surface defects. Each method appears to be limited for detecting some subgroup of defects. The detection techniques may be divided into three main groups: statistical pattern recognition, feature vector extraction and texture/image classification. The methods such as wavelet transform, filtering, morphology and contourlet transform are more effective for pre-processing tasks. Others including statistical methods, neural networks and model-based algorithms can be applied to extract the surface defects. Although, statistical methods are often appropriate for identification of large defects such as Spots, but techniques such as wavelet processing provide an acceptable response for detection of small defects such as Pinhole. A thorough survey is made in this paper on the existing algorithms in each subgroup. Also, the evaluation parameters are discussed including supervised and unsupervised parameters. Using various performance parameters, different defect detection algorithms are compared and evaluated.
97 citations
TL;DR: Both visual analysis and quantitative evaluation of experimental results shows the superiority of proposed method as compared to other methods.
Abstract: Fusion of CT and MR images allows simultaneous visualization of details of bony anatomy provided by CT image and details of soft tissue anatomy provided by MR image. This helps the radiologist for the precise diagnosis of disease and for more effective interventional treatment procedures. This paper aims at designing an effective CT and MR image fusion method. In the proposed method, first source images are decomposed by using nonsubsampled contourlet transform (NSCT) which is a shift-invariant, multiresolution and multidirection image decomposition transform. Maximum entropy of square of the coefficients with in a local window is used for low-frequency sub-band coefficient selection. Maximum weighted sum-modified Laplacian is used for high-frequency sub-bands coefficient selection. Finally fused image is obtained through inverse NSCT. CT and MR images of different cases have been used to test the proposed method and results are compared with those of the other conventional image fusion methods. Both visual analysis and quantitative evaluation of experimental results shows the superiority of proposed method as compared to other methods.
84 citations
TL;DR: A novel multiplicative watermarking scheme in the contourlet domain using the univariate and bivariate alpha-stable distributions is proposed and the robustness of the proposed bivariate Cauchy detector against various kinds of attacks is studied and shown to be superior to that of the generalized Gaussian detector.
Abstract: In the past decade, several schemes for digital image watermarking have been proposed to protect the copyright of an image document or to provide proof of ownership in some identifiable fashion. This paper proposes a novel multiplicative watermarking scheme in the contourlet domain. The effectiveness of a watermark detector depends highly on the modeling of the transform-domain coefficients. In view of this, we first investigate the modeling of the contourlet coefficients by the alpha-stable distributions. It is shown that the univariate alpha-stable distribution fits the empirical data more accurately than the formerly used distributions, such as the generalized Gaussian and Laplacian, do. We also show that the bivariate alpha-stable distribution can capture the across scale dependencies of the contourlet coefficients. Motivated by the modeling results, a blind watermark detector in the contourlet domain is designed by using the univariate and bivariate alpha-stable distributions. It is shown that the detectors based on both of these distributions provide higher detection rates than that based on the generalized Gaussian distribution does. However, a watermark detector designed based on the alpha-stable distribution with a value of its parameter α other than 1 or 2 is computationally expensive because of the lack of a closed-form expression for the distribution in this case. Therefore, a watermark detector is designed based on the bivariate Cauchy member of the alpha-stable family for which α = 1 . The resulting design yields a significantly reduced-complexity detector and provides a performance that is much superior to that of the GG detector and very close to that of the detector corresponding to the best-fit alpha-stable distribution. The robustness of the proposed bivariate Cauchy detector against various kinds of attacks, such as noise, filtering, and compression, is studied and shown to be superior to that of the generalized Gaussian detector.
80 citations
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TL;DR: Wang et al. as mentioned in this paper proposed a new framework for image compressive sensing recovery via structural group sparse representation (SGSR) modeling, which enforces image sparsity and self-similarity simultaneously under a unified framework in an adaptive group domain.
Abstract: Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain. Most of conventional CS recovery approaches, however, exploited a set of fixed bases (e.g. DCT, wavelet, contourlet and gradient domain) for the entirety of a signal, which are irrespective of the nonstationarity of natural signals and cannot achieve high enough degree of sparsity, thus resulting in poor rate-distortion performance. In this paper, we propose a new framework for image compressive sensing recovery via structural group sparse representation (SGSR) modeling, which enforces image sparsity and self-similarity simultaneously under a unified framework in an adaptive group domain, thus greatly confining the CS solution space. In addition, an efficient iterative shrinkage/thresholding algorithm based technique is developed to solve the above optimization problem. Experimental results demonstrate that the novel CS recovery strategy achieves significant performance improvements over the current state-of-the-art schemes and exhibits nice convergence.
65 citations
01 Nov 2014
TL;DR: The proposed technique provides a fused image with better edges and information content from human visual system (HVS) point of view and is found to be superior than that of Daubechies complex wavelet transform (DCxWT).
Abstract: Fusion of various images aids the rejuvenation of complementary attributes of the images. Similarly, medical image fusion constructs a composite image comprehending significant traits from multimodal source images. Current work exhibits medical image fusion utilizing Laplacian Pyramid (LP) employing DCT. LP decomposes the source medical images as different low pass filtered images, resembling a pyramidal structure. As the pyramidal level of decomposition increases, the quality of the fused image also increases. The proposed technique provides a fused image with better edges and information content from human visual system (HVS) point of view. Qualitative and quantitative analysis of the proposed technique is found to be superior than that of Daubechies complex wavelet transform (DCxWT).
60 citations
TL;DR: A redundant version of the CT is proposed to be used, which describes texture structures more accurately and allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions.
Abstract: In this paper, we develop a new framework for contourlet-based statistical modeling using finite Mixtures of Generalized Gaussian distributions ( MoGG) On the one hand, given the rich directional information provided by the contourlet transform (CT), we propose to use a redundant version of the CT, which describes texture structures more accurately On the other hand, we use MoGG modeling of contourlet coefficients distribution, which allows for precise capturing of a wide range of histogram shapes and provides better description and discrimination of texture than single probability density functions (pdfs) Moreover, we propose three applications for the proposed approach, namely: (1) texture retrieval, (2) fabric texture defect detection, and 3) infrared (IR) face recognition We compare two implementations of the CT: standard CT ( SCT) and redundant CT ( RCT) We show that the proposed approach yields better results in the applications studied compared to recent state-of-the-art methods
53 citations
TL;DR: A novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena.
Abstract: Multimodal medical image fusion is a powerful tool in clinical applications such as noninvasive diagnosis, image-guided radiotherapy, and treatment planning. In this paper, a novel nonsubsampled Contourlet transform (NSCT) based method for multimodal medical image fusion is presented, which is approximately shift invariant and can effectively suppress the pseudo-Gibbs phenomena. The source medical images are initially transformed by NSCT followed by fusing low- and high-frequency components. The phase congruency that can provide a contrast and brightness-invariant representation is applied to fuse low-frequency coefficients, whereas the Log-Gabor energy that can efficiently determine the frequency coefficients from the clear and detail parts is employed to fuse the high-frequency coefficients. The proposed fusion method has been compared with the discrete wavelet transform (DWT), the fast discrete curvelet transform (FDCT), and the dual tree complex wavelet transform (DTCWT) based image fusion methods and other NSCT-based methods. Visually and quantitatively experimental results indicate that the proposed fusion method can obtain more effective and accurate fusion results of multimodal medical images than other algorithms. Further, the applicability of the proposed method has been testified by carrying out a clinical example on a woman affected with recurrent tumor images.
51 citations
TL;DR: Two pan-sharpening methods based on the non-subsampled contourlet transform (NSCT) are proposed that preserves both spectral and spatial qualities while decreasing computation time and results demonstrate the efficiency of the proposed methods.
Abstract: Two pan-sharpening methods based on the non-subsampled contourlet transform (NSCT) are proposed. NSCT is very efficient in representing the directional information and capturing intrinsic geometrical structures of the objects. It has characteristics of high resolution, shift-invariance, and high directionality. In the proposed methods, a given number of decomposition levels are used for multispectral (MS) images while a higher number of decomposition levels are used for Pan images relatively to the ratio of the Pan pixel size to the MS pixel size. This preserves both spectral and spatial qualities while decreasing computation time. Moreover, upsampling of MS images is performed after NSCT and not before. By applying upsampling after NSCT, structures and detail information of the MS images are more likely to be preserved and thus stay more distinguishable. Hence, we propose to exploit this property in pan-sharpening by fusing it with detail information provided by the Pan image at the same fine level. The proposed methods are tested on WorldView-2 datasets and compared with the standard pan-sharpening technique. Visual and quantitative results demonstrate the efficiency of the proposed methods. Both spectral and spatial qualities have been improved.
TL;DR: In this article, a novel image fusion technique based on NSST (non-sub sampled shearlet transform) is presented, aiming at resolving the fusion problem of spatially gray-scale visual light and infrared images.
TL;DR: A new hybrid color image segmentation approach, which attempts two different transforms for texture representation and extraction, and the 2-D discrete wavelet transform and the contourlet transform that represents boundaries even more accurately are applied.
Abstract: This paper presents a new hybrid color image segmentation approach, which attempts two different transforms for texture representation and extraction. The 2-D discrete wavelet transform that can express the variance in frequency and direction of textures, and the contourlet transform that represents boundaries even more accurately are applied in our algorithm. The whole segmentation algorithm contains three stages. First, an adaptive color quantization scheme is utilized to obtain a coarse image representation. Then, the tiny regions are combined based on color information. Third, the proposed energy transform function is used as a criterion for image segmentation. The motivation of the proposed method is to obtain the complete and significant objects in the image. Ultimately, according to our experiments on the Berkeley segmentation database, our techniques have more reasonable and robust results than other two widely adopted image segmentation algorithms, and our method with contourlet transform has better performance than wavelet transform.
TL;DR: ShearLab 3D as discussed by the authors is a CUDA-based universal shearlet system for 2D and 3D denoising, inpainting, and feature extraction.
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 of sparsely approximating and also of 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 paper, 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 reseaerch, all scripts are accessible on www.ShearLab.org.
TL;DR: Experimental results show that the TIDFT outperforms some other frame-based Denoising methods, such as contourlet and shearlet, and is competitive to the state-of-the-art denoising approaches.
Abstract: This paper is devoted to the study of a directional lifting transform for wavelet frames. A nonsubsampled lifting structure is developed to maintain the translation invariance as it is an important property in image denoising. Then, the directionality of the lifting-based tight frame is explicitly discussed, followed by a specific translation invariant directional framelet transform (TIDFT). The TIDFT has two framelets ψ1, ψ2 with vanishing moments of order two and one respectively, which are able to detect singularities in a given direction set. It provides an efficient and sparse representation for images containing rich textures along with properties of fast implementation and perfect reconstruction. In addition, an adaptive block-wise orientation estimation method based on Gabor filters is presented instead of the conventional minimization of residuals. Furthermore, the TIDFT is utilized to exploit the capability of image denoising, incorporating the MAP estimator for multivariate exponential distribution. Consequently, the TIDFT is able to eliminate the noise effectively while preserving the textures simultaneously. Experimental results show that the TIDFT outperforms some other frame-based denoising methods, such as contourlet and shearlet, and is competitive to the state-of-the-art denoising approaches.
TL;DR: This study presents an ‘inversion attack’ resilient zero-watermarking system, in the hybrid Contourlet transform – singular value decomposition domain for medical image authentication, that preserves the fidelity of the host image without introducing any artefacts and employs triangular number generating function and Hu's image invariants to confront ‘ inversion attacks’.
Abstract: Medical images are watermarked with patient data to enforce patient authentication and identification in radiology practices. In addition to common threats such as signal processing and geometric attacks, medical image watermarking systems are susceptible to a new class of threats called ‘inversion attack’, leading to ambiguities in establishing rightful ownership. This study presents an ‘inversion attack’ resilient zero-watermarking system, in the hybrid Contourlet transform – singular value decomposition domain for medical image authentication. This scheme preserves the fidelity of the host image without introducing any artefacts and employs triangular number generating function and Hu's image invariants to confront ‘inversion attacks’. The performance of the system is evaluated with medical images of different modalities and a quick response code watermark that contains patient data. The experimental results demonstrate the robustness of the system against ‘ambiguity attacks’ and signify its appropriateness for secured medical image exchange between remote radiologists.
TL;DR: The proposed fusion algorithm based on neighborhood characteristic and regionalization in NSCT (Nonsubsampled Contourlet Transform) domain can leave enough information in the original images and its details, and the fused images have better visual effects.
TL;DR: Experimental results demonstrate that the proposed image watermarking is not only secure and invisible, but also robust against a variety of attacks.
Abstract: In this paper, a novel secure optimal image watermarking scheme using an encrypted gyrator transform computer generated hologram (CGH) in the contourlet domain is presented. A new encrypted CGH technique, which is based on the gyrator transform, the random phase mask, the three-step phase-shifting interferometry and the Fibonacci transform, has been proposed to produce a hologram of a watermark first. With the huge key space of the encrypted CGH, the security strength of the watermarking system is enhanced. To achieve better imperceptibility, an improved quantization embedding algorithm is proposed to embed the encrypted CGH into the low frequency sub-band of the contourlet-transformed host image. In order to obtain the highest possible robustness without losing the imperceptibility, particle swarm optimization algorithm is employed to search the optimal embedding parameter of the watermarking system. In comparison with other method, the proposed watermarking scheme offers better performances for both imperceptibility and robustness. Experimental results demonstrate that the proposed image watermarking is not only secure and invisible, but also robust against a variety of attacks.
TL;DR: Fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images, which was compared with six existing methods.
Abstract: Image fusion means to integrate information from one image to another image. Medical images according to the nature of the images are divided into structural (such as CT and MRI) and functional (such as SPECT, PET). This article fused MRI and PET images and the purpose is adding structural information from MRI to functional information of PET images. The images decomposed with Nonsubsampled Contourlet Transform and then two images were fused with applying fusion rules. The coefficients of the low frequency band are combined by a maximal energy rule and coefficients of the high frequency bands are combined by a maximal variance rule. Finally, visual and quantitative criteria were used to evaluate the fusion result. In visual evaluation the opinion of two radiologists was used and in quantitative evaluation the proposed fusion method was compared with six existing methods and used criteria were entropy, mutual information, discrepancy and overall performance.
01 Jun 2014
TL;DR: It is shown that the alpha-stable family of distributions provides a more accurate model to the contourlet subband coefficients than the formerly used distributions, namely, the generalized Gaussian and Laplacian distributions, both in terms of the subjective measure of the Kolmogorov-Smirnov distance and the objective measure of comparing the log-scale histograms.
Abstract: It is known that the contourlet coefficients of images have non-Gaussian property and heavy tails. In view of this, an appropriate distribution to model the statistics of the contourlet coefficients would be the one having large peaks, and tails heavier than that of a Gaussian PDF, i.e., a heavy-tailed PDF. This paper proposes a new image modeling in the contourlet domain, where the magnitudes of the coefficients are modeled by a symmetric alpha-stable distribution which is best suited for modeling transform coefficients with a high non-Gaussian property and heavy tails. It is shown that the alpha-stable family of distributions provides a more accurate model to the contourlet subband coefficients than the formerly used distributions, namely, the generalized Gaussian and Laplacian distributions, both in terms of the subjective measure of the Kolmogorov-Smirnov distance and the objective measure of comparing the log-scale histograms.
TL;DR: The proposed automatic enhancement method can efficiently enhance the edge features and contrast of SAR images and reduce the speckle noises and outperforms the wavelet-based and NSCT-based non-automatic enhancement methods.
TL;DR: A blind and highly robust watermarking scheme method for color images by combining the advantages of both spatial and frequency domain that achieves high transparency, imperceptibility and robustness compared to some of the existing schemes.
04 May 2014
TL;DR: It is shown that a symmetric normal inverseGaussian distribution is more suitable for modeling the contourlet coefficients than formerly-used generalized Gaussian distribution for reducing noise in images corrupted by additive white Gaussian noise.
Abstract: A new contourlet-based method is introduced for reducing noise in images corrupted by additive white Gaussian noise It is shown that a symmetric normal inverse Gaussian distribution is more suitable for modeling the contourlet coefficients than formerly-used generalized Gaussian distribution To estimate the noise-free coefficients, a Bayesian maximum a posteriori estimator is developed utilizing the proposed distribution In order to estimate the parameters of the distribution, a moment-based technique is used The performance of the proposed method is studied using typical noise-free images corrupted with simulated noise and compared with that of the other state-of-the-art methods It is shown that compared with other denoising techniques, the proposed method gives higher values of the peak signal-to-noise ratio and provides images of good visual quality
TL;DR: Experimental results of proposed despeckling algorithm, based on non-subsampled contourlet transform, show that the proposed method is able to preserve edges and image structural details compared with existing methods.
Abstract: Speckle noise reduction is an important preprocessing stage for ultrasound medical image processing. In this paper, a despeckling algorithm is proposed based on non-subsampled contourlet transform. This transform has the property of high directionality, anisotropy and translation invariance, which can be controlled by non-subsampled filter banks. This study aims to denoise the speckle noise in ultrasound images using adaptive binary morphological operations, in order to preserve edges, contours and textures. In morphological operations, structural element plays an important role for image enhancement. In this work, different shapes of structural element have been analysed and filtering parameters have been changed adaptively depending on the nature of the image and the amount of noise in the image. Experimental results of proposed method for natural images, Field II simulated images and real ultrasound images, show that the proposed method is able to preserve edges and image structural details compared with existing methods.
TL;DR: A new robust image watermarking based on EMs invariants in nonsubsampled contourlet transform (NSCT) domain is proposed and the digital watermark is embedded by quantizing the modulus of the selected EMs.
TL;DR: This work introduces and applies a novel multiscale image decomposition algorithm for the efficient digital implementation of wavelets with composite dilations, and provides consistent improvements upon competing state-of-the-art methods.
Abstract: It is widely recognized that the performance of many image processing algorithms can be significantly improved by applying multiscale image representations with the ability to handle very efficiently directional and other geometric features. Wavelets with composite dilations offer a flexible and especially effective framework for the construction of such representations. Unlike traditional wavelets, this approach enables the construction of waveforms ranging not only over various scales and locations but also over various orientations and other orthogonal transformations. Several useful constructions are derived from this approach, including the well-known shearlet representation and new ones, introduced in this paper. In this work, we introduce and apply a novel multiscale image decomposition algorithm for the efficient digital implementation of wavelets with composite dilations. Due to its ability to handle geometric features efficiently, our new image processing algorithms provide consistent improvements upon competing state-of-the-art methods, as illustrated on a number of image denoising and image enhancement demonstrations.
17 Nov 2014
TL;DR: Experimental results demonstrate that the proposed fusion method owns clear advantages over the fusion method based on NSCT or SR individually in terms of both visual quality and objective assessments.
Abstract: In this paper, we present a novel medical image fusion method by taking the complementary advantages of two powerful image representation theories: nonsubsampled contourlet transform (NSCT) and sparse representation (SR). In our fusion algorithm, the NSCT is firstly performed on each of the pre-registered source images to obtain the low-pass and high-pass coefficients. Then, the low-pass bands are merged with a SR-based fusion approach, and the high-pass bands are fused by employing the absolute values of coefficients as activity level measurement. Finally, the fused image is obtained by performing inverse NSCT on the merged coefficients. Several sets of medical source images with different combinations of modalities are used to test the effectiveness of the proposed method. Experimental results demonstrate that our method owns clear advantages over the fusion method based on NSCT or SR individually in terms of both visual quality and objective assessments.
21 Feb 2014
TL;DR: The result of experiment shows that this method could add the polarization characteristics into the original intensity image, increase the image contrast and clarity, and make the overall visual effect better.
Abstract: Based on characteristics that infrared polarization image can restrain background noise greatly and can be more sensitive to target edge information, a polarization image fusion algorithm based on oriented Laplacian pyramid is proposed in this paper. The method is mainly used in image fusion between the infrared radiation intensity image I and degree of polarization image P in order to increase the amount of information of the image. First, each Gaussian pyramid level is decomposed in different directions to get oriented Laplacian pyramid. Second, the oriented Laplacian pyramid level images of the image I and the image P are fused with the criterion of direction gradient to get fused oriented Laplacian pyramid level images. At last, the fused image is acquired by image reconstruction. The result of experiment shows that this method could add the polarization characteristics into the original intensity image, increase the image contrast and clarity, and make the overall visual effect better.
TL;DR: Experimental results show that the proposed catenary image enhancement method based on the wavelet-based contourlet transform (WBCT) with cycle translation has the advantages of preserving image edge details and texture and outperforms the traditional methods.
TL;DR: Experimental results show that the quality of pan-sharpening of remote-sensing images of different spatial resolution ratios using the APCA–NSCT method is affected by NSCT parameters.
Abstract: Recent studies show that hybrid panchromatic sharpening (pan-sharpening) methods using the non-sub-sampled contourlet transform (NSCT) and classical pan-sharpening methods such as intensity, hue and saturation (IHS), principal component analysis (PCA), and adaptive principal component analysis (APCA) reduce spectral distortion in pan-sharpened images. The NSCT is a shift-invariant multi-resolution decomposition. It is based on non-sub-sampled pyramid (NSP) decomposition and non-sub-sampled directional filter banks (NSDFBs). We compare the performance of the APCA–NSCT using different NSP filters, NSDFB filters, number of decomposition levels, and number of orientations in each level on SPOT 4 data with a spatial resolution ratio of 1:2, and Quickbird data with a spatial resolution ratio of 1:4. Experimental results show that the quality of pan-sharpening of remote-sensing images of different spatial resolution ratios using the APCA–NSCT method is affected by NSCT parameters. For the NSP, the ‘maxflat’ filt...