- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Multi-source Remote Sensing Image Registration Based on Contourlet Transform and Multiple Feature Fusion
摘要： Image registration is an indispensable component in multi-source remote sensing image processing. In this paper, we put forward a remote sensing image registration method by including an improved multi-scale and multi-direction Harris algorithm and a novel compound feature. Multi-scale circle Gaussian combined invariant moments and multi-direction gray level co-occurrence matrix are extracted as features for image matching. The proposed algorithm is evaluated on numerous multi-source remote sensor images with noise and illumination changes. Extensive experimental studies prove that our proposed method is capable of receiving stable and even distribution of key points as well as obtaining robust and accurate correspondence matches. It is a promising scheme in multi-source remote sensing image registration.
关键词： contourlet transform,multi-source remote sensing image registration,multi-direction gray level co-occurrence matrix,multi-scale circle Gaussian combined invariant moment,Feature fusion
更新于2025-09-23 15:23:52
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Hybrid secure and robust image watermarking scheme based on SVD and sharp frequency localized contourlet transform
摘要： In this paper, using singular value decomposition (SVD) and sharp frequency localized contourlet transform (SFLCT) a secure and robust image watermarking procedure is introduced. The SVD and SFLCT are applied on both watermark and original images and using the properties of the SVD and utilizing the advantages of the SFLCT, noticeable results of the watermarking requirements are obtained. Since most of the SVD based watermarking schemes are not resistant against ambiguity attacks and suffer from the false positive problem, this objection is resolved without adding extra steps to the watermarking algorithm and the suggested scheme is secure and resistant against ambiguity attacks. The simulation of the scheme is implemented and its robustness against various types of attacks is experimented. In comparison with some of the recent schemes this procedure shows high imperceptibility, capacity and robustness and these features make the scheme a suitable choice for the image processing applications.
关键词： Ambiguity attacks,Contourlet transform,Image processing,Singular values,Image watermarking
更新于2025-09-23 15:22:29
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - A Multi-Direction Subbands and Deep Neural Networks Bassed Pan-Sharpening Method
摘要： This paper proposes a pan-sharpening method based on multi-direction subbands and deep neural networks. First, by utilizing the multi-scale and multi-direction properties of the nonsubsampled contourlet transform (NSCT), panchromatic (PAN) image is decomposed into the low frequency subbands in different resolutions and the high frequency subbands in different directions. Pan-sharpening method aims to fuse the high frequency subband coefficients of PAN image and the low frequency subband coefficients of multispectral (MS) image. Second, in order to better extract the feature of the high frequency subbands in different directions of PAN image, the deep neural network (DNN) is trained using the image patches of high frequency subbands of PAN image. Third, in the fusion stage, we exploit NSCT on the principal component of resampled low resolution (LR) MS image. The high frequency subbands of output high resolution (HR) MS image is obtained by forward propagation of the trained DNN, which input is the high frequency subbands of LR MS image. Finally, a new subband set is obtained by fusing the reconstructed high frequency subband and the original low frequency subband of LR MS image. The HR MS image is produced by executing the inverse transform of NSCT and adaptive PCA (A-PCA) on the new subband set. The experimental results show the proposed method outperforms other well-known methods in terms of both objective measurements and visual evaluation.
关键词： adaptive Principal Component Analysis (A-PCA),deep neural network (DNN),pan-sharpening,nonsubsampled contourlet transform (NSCT)
更新于2025-09-23 15:22:29
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Secure Image Compression Approach based on Fusion of 3D Chaotic Maps and Arithmetic Coding
摘要： The advances in digital image processing and communications have created a great demand for real–time secure image transmission over the networks. However, the development of effective, fast and secure dependent image compression encryption systems are still a research problem as the intrinsic features of images such as bulk data capacity and high correlation among pixels hinds the use of the traditional joint encryption compression methods. A new approach is suggested in this paper for partial image encryption compression that adopts chaotic 3D cat map to de-correlate relations among pixels in conjunction with an adaptive thresholding technique that is utilized as a lossy compression technique instead of using complex quantization techniques and also as a substitution technique to increase the security of the cipher image. The proposed scheme is based on employing both of lossless compression with encryption on the most significant part of the image after contourlet transform. However the least significant parts are lossy compressed by employing a simple thresholding rule and arithmetic coding to render the image totally unrecognizable. Due to the weakness of 3D cat map to chosen plain text attack, the suggested scheme incorporates a mechanism to generate random key depending on the contents of the image (context key). Several experiments were done on benchmark images to insure the validity of the proposed technique. The compression analysis and security outcomes indicate that the suggested technique is an efficacious and safe for real time image’s applications.
关键词： Cryptography,Chaotic maps,Joint compression encryption,Contourlet transform,Thresholding,Arithmetic coding
更新于2025-09-23 15:21:01
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Robust automatic classification of benign and malignant microcalcification and mass in digital mammography
摘要： Breast cancer is the most dangerous cancer among women and second mortality among them. Mammography is the efficient methodology used in early finding of breast cancer. However, mammograms requires high amount of skill and there is a possibility of radiologist to misunderstand it. Hence, computer aided diagnosis are used for finding the abnormalities in mammograms. Automated classification of mass and microcalcification system is proposed in this work using NSCT and SVM. The classification of abnormalities is achieved by extracting the microcalcification and mass features from the contourlet coefficients of the image and the results are used as an input to the SVM. The proposed automated system classifies the mammogram as normal or abnormal and result is abnormal, then it classifies the abnormal severity as benign or malignant. The evaluation of the proposed system is conceded on MIAS database. The experimentation result shows that the proposed system contributes improved classification rate.
关键词： mammogram,mass,non-subsampled contourlet transform,SVM,support vector machine,NSCT,benign,microcalcifications,malignant
更新于2025-09-09 09:28:46
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Multi-resolution Image Fusion in Remote Sensing () || Image Fusion: Model Based Approach with Degradation Estimation
摘要： Recently, many researchers have attempted to solve the problem of multi-resolution image fusion by using model based approaches, with emphasis on improving the fused image quality and reducing color distortion [273, 121]. They model the low resolution (LR) MS image as a blurred and noisy version of its ideal high resolution (HR) fused image. Solving the problem of fusion by the model based approach is desirable since the aliasing present due to undersampling of the MS image can be taken care of while modelling. Fusion using the interpolation of MS images and edge-preserving ?lters as given in Chapter 3 do not consider the effect of aliasing which is due to undersampling of MS images. The aliasing in the acquired image causes distortion and, hence, there exists degradation in the LR MS image. In this chapter, we propose a model based approach in which a learning based method is used to obtain the required degradation matrix that accounts for aliasing. Using the proposed model, the ?nal solution is obtained by considering the model as an inverse problem. The proposed approach uses sub-sampled as well as non sub-sampled contourlet transform based learning and a Markov random ?eld (MRF) prior for regularizing the solution.
关键词： model based approach,multi-resolution image fusion,degradation estimation,contourlet transform,Markov random field
更新于2025-09-04 15:30:14
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Multi-resolution Image Fusion in Remote Sensing () || Image Fusion: Application to Super-resolution of Natural Images
摘要： Increasing the spatial resolution of a given test image is of interest to the image processing community since the enhanced resolution of the image has better details when compared to the corresponding low resolution image. Super-resolution (SR) is an algorithmic approach in which a high spatial resolution image is obtained by using single/multiple low resolution observations or by using a database of LR–HR pairs. The linear image formation model discussed for image fusion in Chapter 4 is extended here to obtain an SR image for a given LR test observation. In the image fusion problem, the available Pan image was used in obtaining a high resolution fused image. Similar to the fusion problem, SR is also concerned with the enhancement of spatial resolution. However, we do not have a high resolution image such as a Pan image as an additional observation. Hence, we make use of a database of LR–HR pairs in order to obtain the SR for the given LR observation. Here, we use contourlet based learning to obtain the initial SR estimate which is then used in obtaining the degradation as well as the MRF parameter. Similar to the fusion problem discussed in Chapter 4, an MAP–MRF framework is used to obtain the final SR image.
关键词： image processing,degradation estimation,MAP–MRF framework,Super-resolution,contourlet transform
更新于2025-09-04 15:30:14