Contourlet Research Articles

Study on the application of multi-angle imaging related technology in the construction process

Abstract The mufti-angle imaging technique is proposed to address the wall position offset and the side flaps and gate flaps caused by the sparse array in the ultra-wide band through-wall radar building layout imaging, using the characteristic that the azimuth upward distance profile is similar around the main flap but different around the side flap/gate flap. Firstly, after the backward projection algorithm obtains the wall image of each viewpoint, the mufti-directional two-dimensional matching filter is used to enhance the image of each viewpoint and eliminate the effect of side flaps and raster flaps. Then the images of each viewpoint are decomposed by combining the multistage transform, i.e., the non-down sampling contour let transform, and the obtained low-frequency components and high-frequency components are fused using the corresponding fusion rules, respectively. Finally, the final fused building layout image is obtained by non-down sampling contour let inverse transform. The simulation experimental data show that the running time of the method in this paper is 10.99s. It shows that the proposed method enhances the outline and detail information of the building wall, and the final fused image obtained has prominent wall image, clearer edge contours, and good retention of overall detail information. The quality of image fusion is greatly improved than other methods, which paves the way for subsequent fusion methods including chatterers such as construction building corners and windows and doors.

Image Fusion Based on NSCT and Sparse Representation for Remote Sensing Data

The practice of integrating images from two or more sensors collected from the same area or object is known as image fusion. The goal is to extract more spatial and spectral information from the resulting fused image than from the component images. The images must be fused to improve the spatial and spectral quality of both panchromatic and multispectral images. This study provides a novel picture fusion technique that employs L0 smoothening Filter, Non-subsampled Contour let Transform (NSCT) and Sparse Representation (SR) followed by the Max absolute rule (MAR). The fusion approach is as follows: first, the multispectral and panchromatic images are divided into lower and higher frequency components using the L0 smoothing filter. Then comes the fusion process, which uses an approach that combines NSCT and SR to fuse low frequency components. Similarly, the Max-absolute fusion rule is used to merge high frequency components. Finally, the final image is obtained through the disintegration of fused low and high frequency data. In terms of correlation coefficient, Entropy, spatial frequency, and fusion mutual information, our method outperforms other methods in terms of image quality enhancement and visual evaluation.

SUPPRESSION OF SPECKLE NOISE IN MEDICAL IMAGES VIA SEGMENTATION-GROUPING OF 3D OBJECTS USING SPARSE CONTOURLET REPRESENTATION

Novel filtering method in medical images (MRI and US) that are contaminated by noise consisting of mixture speckle and additive noise is designed in this paper. Proposed method consists of several stages: segmentation of image areas, grouping of similar 2D structures in accordance mutual information (MI) measure, homomorphic transformation, 3D filtering approach based on sparse representation in contourlet (CLT) space with posterior filtering in accordance with MI weights similar 2D structures, and final inverse homomorphic transformation. During numerous experiments, the developed method has confirmed their superiority in term of visual image quality via human visual perception as well as in better criteria values, such as PSNR, SSIM, EPI and alfa for different test MRI and US mages corrupted by speckle noise.

RETRACTED ARTICLE: Research on underwater flexible target recognition algorithm under non-uniform light

In underwater optical detection, due to the introduction of uneven light, the accuracy of underwater target recognition is reduced, especially when identifying underwater flexible targets. In this paper, a new type of depth wish-deformable convolutional neural network structure (DD-CNN) underwater target recognition algorithm is proposed for underwater flexible target recognition under the influence of non-uniform light. Firstly, the underwater invariant extraction algorithm based on non-subsampled contour let transform is used to extract the essential features of underwater images, and the underwater image after extraction is used as the input of the new depth wish-deformable convolutional neural network for target recognition. The experimental results show that the accuracy of using the proposed algorithm to identify underwater flexible targets is improved compared with the accuracy of the original structure, and the network convergence speed is faster.

HYPERSPECTRAL AND MULTISPECTRAL IMAGE FUSION USING NSCT AND FDCT METHODS

Image fusion plays an important role in computer vision, medical imaging, robotics, remote sensing and satellite imagery. Image fusion is a method or a process in which it produce all relevant information in a single image by combining two or more input images. The final image or final output contains more information than a single image. Hyperspectral image known as HIS and Multispectral image fusion known as MSI are used in wide array of application although it originally developed for geology and mining because of high spectral-resolution but it has low spatial-resolution. Non-Subsampled Contour let Transform known as NSCT. Hyperspectral and multispectral Image Fusion using NSCT and FDCT methods are used in this paper and it can be used in various fields like medical imaging, Satellite images, space research and remote sensing.

The Enriched Feature Enhancement Technique for Satellite Image Based on Transforms Using PCNN

The features of the satellite images can be improved by fusing or combining two images with complementary property. By fusing these two images the spatial property of the resultant image is improved. Satellite images are one of the agents that give the features of the earth’s surface. Processing these satellite images will provide more geographical information hidden in the images. This research paper have an detailed insight study of two types of the satellite images one is Panchromatic (PAN) and other Multispectral (MS). The PAN image with high spatial resolution and MS image with spectral resolution are fused to get better resultant output. For fusion process Nonsubsampled Contour let Transform is used to decompose the images into low and high frequency values. Pulse Coupled Neural Network is used to motivate the low frequency pixel and Morphological filter is applied to the edge detected image for finding the features in the images. This is an real time transformations which will give better results in SAR image processing, video processing, stereo based reconstruction of depth and width of the features present in the image.

Segmentation and Vein Extraction in Sclera for Human Identification

In this paper the main contribution is statistical approaches based image segmentation method, and is able to pointout the circular pupil and sclera area, occluding eyelashes and eyelids, and reflections by a technique Image cropping. Proposed segmentation method is followed with investigation of five feature extraction techniques, viz. Statistical approaches, Glare Area Detection, Wavelet transforms, Lifting wavelet transforms and finally Contour let transform to extract features and to encode and decode the particular parameters of the iris into a bit-wise biometric template. A comparative analysis is done for these techniques with the proposed segmentation method. The green channel extraction measure and Separable Power was employed for classification, finally for classification adopting train iris image deviated output image methods is imported out. The proposed iris recognition method that uses Glare Area Detection and database identification for feature extraction performed with a near-perfect identification in different sclera pictures. This proposed system demonstrates that combination of proposed segmentation approach and feature extraction method in this work helps to improve overall performance and significant increase in the sclera recognition accuracy. The algorithm is robust for noisy conditions such as errors, elliptical pupils, excess eyelash occlusion errors and bad contrast.

An improved image denoising method based on contourlet transform and NeighShrink algorithm

An improved image denoising method based on contourlet transform and NeighShrink algorithm

Adaptive Image Fusion Scheme Based on Contourlet Transform and Machine Learning

Adaptive image fusion scheme based on the combination of contourlet transform, Kernel Principal Component Analysis (K-PCA), Support Vector Machine (SVM) and Mutual Information (MI) is proposed. Contourlet is well suited to image fusion scheme because of its properties, such as localization, multiresolution, directionality and anisotropy. K-PCA operates on low frequency subband to extract feature and SVM is applied to high frequency subbands to obtain a composite image with extended information. Moreover, Mutual Information (MI) is used to adjust the contribution of each source image in the final fused image. Performance evaluation is carried out by using recently developed metric, Image Quality Index (IQI). The proposed scheme outperforms previous approaches both subjectively and quantitatively, and this is evident from the experimental results and findings.

DC-QIM based image watermarking method via the contourlet transform

DC-QIM based image watermarking method via the contourlet transform

Directive Contrast based Multimodal Image Fusion in UWT and NSCT Domain

Objectives: The purpose of image fusion is to provide information integrated from different images to eliminate redundancy and contradiction between images. This paper presents medical image fusion using pixel level fusion with multi-level wavelet transform to obtain low frequency and high frequency subbands. This involves a pixel level averaging rule for appropriate fusion to integrate the decomposed image coefficients subbands. Methods: A two-stage multimodal fusion framework uses the cascaded combination of Un-decimated Wavelet Transform (UWT) and Non Sub-sampled Contour let Transform (NSCT) domains are used. Findings: This is to improve upon the shift variance, directionality, and phase information in the finally fused image. Applications/Improvements: A mathematical analysis of fused images is done using dedicated fusion metrics. The system performance is evaluated by using the parameters such as Peak signal to noise ratio, correlation and entropy.

A Hybrid the Nonsubsampled Contourlet Transform and Homomorphic Filtering for Enhancing Mammograms

Mammogram is important for early breast cancer detection. But due to the low contrast of microcalcifications and noise, it is difficult to detect microcalcification. This paper presents a comparative study in digital mammography image enhancement based on three different algorithms: homomorphic filtering, unsharp masking and our proposed methods. This latter use a hybrid method Combining contourlet and homomorphic filtering. Performance of the given technique has been measured in terms of distribution separation measure (DSM), target-to-background enhancement measure based on standard deviation (TBES) and target-to-background enhancement measure based on entropy (TBEE). The proposed methods were tested with the referents mammography data Base MiniMIAS. Experimental results show that the proposed method improves the visibility of microcalcification.

Multi Temporal SAR Image Analysis using NSCT Fusion and Supervised Classifier for Change Detection

The project presents change detection approach for synthetic aperture radar (SAR) images based on an image fusion and supervised classifier system. The image fusion technique will be introduced to generate a difference image by using complementary information from a mean-ratio image and a log-ratio image. NSCT (Non- subsampled contourlet transform) fusion rules based on an average operator and minimum local area gradient are chosen to fuse the contourlet coefficients for a low-frequency band and a high-frequency band respectively, to restrain the background information and to enhance the information of changed regions in the fused difference image. For the remote sensing images, differencing(subtraction operator) and ratioing (ratio operator) are well-known techniques for producing a difference image. In differencing, changes are measured by subtracting the intensity values pixel by pixel between the considered couple of temporal images. In ratioing, changes are obtained by applying a pixel-by-pixel ratio operator to the considered couple of temporal images. In the case of SAR images, the ratio operator is typically used instead of the subtraction operator since the image differencing technique is not adapted to the statistics of SAR images. An artificial neural network type multi-layer perceptron or back propagation with feed forward network will be proposed for classifying changed and unchanged regions in the fused difference image. This classifier comes under supervised segmentation which is worked based on training cum classification. The results will be proven that ratioing generates better difference image for change detection, using supervised classifier segmentation approach and efficiency of this algorithm will be exhibited by sensitivity and correlation evaluation.

Contourlet Transform Based Shot Boundary Detection

Shot boundary detection is used to summarize the video in the video content management system so that particular content or required scene can be extracted from a given video and also observers or users can easily find summary of the given video in short time instead of time spending in seeing full length video. Also shot boundaries can be used for video indexing, browsing, and retrieval. So to extract such key frames (shot boundaries) from a given video needs a robust technique with high accuracy and low error rate. In this research work, we proposed a robust technique to find shot boundary using feature vectors, which are obtained from features calculated for each sub-band in the contour let transform domain. The feature vector has the energy, standard deviation, and histogram similarity as the features of each sub-band. The experimental results proved that this novel and robust method had more accuracy rate and low error rate.

Recognizing driving postures by combined features of contourlet transform and edge orientation histogram, and random subspace classifier ensembles

In order to develop Human-centered Driver Assistance Systems (HDAS), an efficient Combined Feature (CF) extraction approach from Contourlet Transform (CT) and Edge Orientation Histogram (EOH) is pr...

Medical Image Compression in WBCT Domain Using Fast FIC and SPIHT Coding

Fractal image coding encodes the input image with low bit rate and decodes the same with the reasonable quality of reconstruction. But, FIC has the demerit of longer time taken for encoding. In this paper, a fractal and SPIHT encoding of WBCT coefficients is proposed. Fast fractal encoding using particle swarm optimization (PSO) is applied to the low pass sub band of wavelet based contourlet transformed image and a modified set partitioning in hierarchical trees (SPIHT) coding is applied on the remaining coefficients. PSO technique and reduced domain pool size will speed up the fractal encoder, whereas the WBCT coefficients preserve the image quality in case of smooth contoured images. Furthermore, image details and wavelet progressive transmission characteristics are maintained. Experimental results prove that the proposed algorithm provides better quality of reconstruction with reduced bit rate.

Efficient Multimodal Biometric System Based on Feature Level Fusion of Palmprint and Finger Vein

Biometric authentication is playing a vital role in providing security and privacy. This paper presents a contemporary approach for identifying an individual using the multimodal biometrics has great demands to overcome the issues involved in single trait system. Finger vein and palmprint biometric is a promising technology, now-a-days widely used because of its important features such as resistant to criminal tampering, high accuracy, ease of feature extraction and fast authentication speed. The features extracted from the preprocessed finger vein and palmprint images using Contourlet Transform reduce the overall dimensionality and computational complexity. Fusion at feature level utilizes the fused feature vectors of finger vein and palmprint using the Discrete Stationary Wavelet Transform (DSWT) to improve the overall performance of the multimodal biometric system. Integrating the biometric traits increases the robustness of the person identification and reduces fraudulent access. Experimental results based on homologous database demonstrate that the proposed system is very efficient to reduce the False Rejection Rate (FRR) and False Acceptance Rate (FAR).

A New Image Watermarking Algorithm Using NSCT and Harris Detector in Green Manufacturing

Based on multistage theory and NSCT, a new feature-based image watermarking scheme is proposed in this paper. Firstly, the multistage Harris detector is utilized to extract steady feature points from the host image; then, the local feature regions (LFR) are ascertained adaptively according to the feature scale theory, and they are scaled to a standard size; finally, the digital watermark are embedded into no subsample contour let low frequency area, in the low frequency regions made pseudo-Zernike moment calculation. Experimental results show that the proposed scheme is not only invisible and robust against common signals processing such as median filtering, sharpening, noise adding and JPEG compression et al, but also robust against the unclassified geometric attacks such as rotation, translation, scaling, row or column removal, shearing, local geometric distortion and combination attacks et al.

Registration Technique for High-resolution Remote Sensing Images based on SIFT and Contourlet Transform

Registration Technique for High-resolution Remote Sensing Images based on SIFT and Contourlet Transform

Sampling adaptive block compressed sensing reconstruction algorithm for images based on edge detection

In this paper, a sampling adaptive for block compressed sensing with smooth projected Landweber based on edge detection (SA-BCS-SPL-ED) image reconstruction algorithm is presented. This algorithm takes full advantage of the characteristics of the block compressed sensing, which assigns a sampling rate depending on its texture complexity of each block. The block complexity is measured by the variance of its texture gradient, big variance with high sampling rates and small variance with low sampling rates. Meanwhile, in order to avoid over-sampling and sub-sampling, we set up the maximum sampling rate and the minimum sampling rate for each block. Through iterative algorithm, the actual sampling rate of the whole image approximately equals to the set up value. In aspects of the directional transforms, discrete cosine transform (DCT), dual-tree discrete wavelet transform (DDWT), discrete wavelet transform (DWT) and Contourlet (CT) are used in experiments. Experimental results show that compared to block compressed sensing with smooth projected Landweber (BCS-SPL), the proposed algorithm is much better with simple texture images and even complicated texture images at the same sampling rate. Besides, SA-BCS-SPL-ED-DDWT is quite good for the most of images while the SA-BCS-SPL-ED-CT is likely better only for more-complicated texture images.