Nonsubsampled Contourlet Transform Research Articles

Absolute Transformation and Clahe Based High Performance Lucid Proposal for Image Processing

Image fusion is a one of the enhancement technique which is used to take the decision the images by the various types of sensors. Image fusion is nothing but the combination of two images which is helps to improve the quality of the image. In this paper, visible image and Infrared image are combined to acquire the informative image. Before and after image fusion, a new transformation technique is introduced to improve the quality of the image. To prove the quality of the image after applying new transformation technique, the fusion is done by four different techniques is used like Discrete Cosine Transform (DCT), Discrete Wavelet Transform (DWT), Non-Subsampled Contourlet Transform (NSCT) and Dual Tree Complex Wavelet Transform (DT-CWT). The comparison of following parameter values such as Entropy, Standard deviation, Mean gradient, Average pixel intensity and spatial frequency shows that proposed method is better to improve the image quality.

Change Detection in Multispectral Remote Sensing Images with Leader Intelligence PSO and NSCT Feature Fusion

Change detection (CD) using Remote sensing images have been a challenging problem over the years. Particularly in the unsupervised domain it is even more difficult. A novel automatic change detection technique in the unsupervised framework is proposed to address the real challenges involved in remote sensing change detection. As the accuracy of change map is highly dependent on quality of difference image (DI), a set of Normalized difference images and a complementary set of Normalized Ratio images are fused in the Nonsubsampled Contourlet Transform (NSCT) domain to generate high quality difference images. The NSCT is chosen as it is efficient in suppressing noise by utilizing its unique characteristics such as multidirectionality and shift-invariance that are suitable for change detection. The low frequency sub bands are fused by averaging to combine the complementary information in the two DIs, and, the higher frequency sub bands are merged by minimum energy rule, for preserving the edges and salient features in the image. By employing a novel Particle Swarm Optimization algorithm with Leader Intelligence (LIPSO), change maps are generated from fused sub bands in two different ways: (i) single spectral band, and (ii) combination of spectral bands. In LIPSO, the concept of leader and followers has been modified with intelligent particles performing Lévy flight randomly for better exploration, to achieve global optima. The proposed method achieved an overall accuracy of 99.64%, 98.49% and 97.66% on the three datasets considered, which is very high. The results have been compared with relevant algorithms. The quantitative metrics demonstrate the superiority of the proposed techniques over the other methods and are found to be statistically significant with McNemar’s test. Visual quality of the results also corroborate the superiority of the proposed method.

A novel multi-source image fusion method for pig-body multi-feature detection in NSCT domain

The multi-source image fusion has been a hot topic during the recent years because of its higher detection rate. To improve the accuracy of pig-body multi-feature detection, a multi-source image fusion method was adopted in this field. However, the traditional multi-source image fusion methods could not obtain better contrast and more details of the fused image. To better detect shape and temperature feature of pig-body, a novel infrared and visible image fusion method was proposed in non-subsampled contourlet transform (NSCT) domain and named NSCT-GF-IAG. Through this technique, the visible and infrared images were first decomposed into a series of multi-scale and multi-directional sub-bands using NSCT. Then, to better represent the fine-scale of texture information and coarse-scale detail information, Gabor filter with even-symmetry and improved average gradient (IAG) were employed to fuse low-frequency and high-frequency sub-bands, respectively. Next, the fused coefficients were reconstructed into a final fusion image by inverse NSCT. Finally, the shape feature of pig-body was obtained by automatic threshold segmentation and optimized by morphological processing. Moreover, the highest temperature was extracted based on shape segmentation of pig-body. Experimental results showed that the proposed fusion method for detecting multi-feature was capable of achieving 2.175–5.129% higher average segmentation rate than the prevailing conventional methods. Besides this, the proposed method also improved efficiency in terms of time consumption.

A novel multi-modal medical image fusion algorithm

In order to improve the contrast of image fusion and highlight the unique characteristics of medical images, a multi-modal medical image fusion algorithm in the framework of non-subsampled contourlet transform (NSCT) is proposed in this paper. Firstly, the computed tomography images and magnetic resonance image are decomposed into low- and high-frequency sub-bands through the NSCT of multi-scale geometric transformation; secondly, for the low-frequency sub-band, the local area standard deviation method is selected or the fusion, while for the high-frequency sub-band, an adaptive pulse coupling neural network model is constructed and the fusion rules are set by the cumulative ignition times of iterative operation in the network; finally, the fusion image is obtained through image reconstruction. Experimental results show that the fusion results of the algorithm in this paper can improve the image fusion quality significantly and it has certain advantages in both visual effects and objective evaluation indexes, which provides a more reliable basis for clinical diagnosis and treatment of diseases.

Nonsubsampled Contourlet Transform based Video Compression using Huffman and Run Length Encoding for Multimedia Applications

Nonsubsampled Contourlet Transform based Video Compression using Huffman and Run Length Encoding for Multimedia Applications

Nonsubsampled contourlet transform-based conditional random field for SAR images segmentation

In this paper, we propose a new texture-based conditional random field (CRF) for Synthetic Aperture Radar (SAR) image segmentation. In our proposed algorithm to overcome the limitations of the intensity-based features, feature extraction is performed in the contourlet transform domain. We use the nonsubsampled contourlet transform (NSCT) as an overcomplete transform which compensates the shortcomings of the traditional contourlet. Applying the generalized Gaussian distribution (GGD) for the statistical description of NSCT coefficients, we simultaneously extract proper statistics from SAR image in the conditional random field model and overcome the speckle effects in the intensity-based features. In this way, not only there is no need to consider an additional term in unary function to model the statistics of SAR image but also, we no longer need to calculate the several criteria based on the histogram of speckled gray levels. Experimental results show the superiority of NSCT compared to the other transform-based features such as wavelet and also demonstrate the improvement of the accuracy in contrast to the schemes which are based on the intensity in the CRF model.

Fractal dimension based parameter adaptive dual channel PCNN for multi-focus image fusion

Multi-focus image fusion combines the focused parts of multiple images of the same scene to generate a fully focused image. Dual Channel Pulse coupled neural network (DCPCNN) is frequently used in image fusion framework due to its characteristics like global coupling, pulse synchronization of neurons and simultaneous processing of two images. However, it suffers from the manual setting of its parameters. This paper proposes a transform domain multi-focus image fusion method based on a novel parameter adaptive DCPCNN (PA-DCPCNN) model, in which the parameters are adaptively estimated using the inputs. Moreover, the linking strengths of the PA-DCPCNN model are computed per neuron using a new fractal dimension based focus measure (FDFM). First, the multi-scale and multi-direction decomposition of source images are performed using non-subsampled contourlet transform (NSCT) to obtain the low-pass sub-band and a number of band-pass directional sub-bands. Second, the band-pass directional sub-bands of the source images are fused using the PA-DCPCNN model. Third, the low-pass sub-bands of source images are combined using multi-scale morphological gradient (MSMG) and FDFM to generate the fused low-pass sub-band. Finally, the fused image is obtained by applying inverse NSCT to the fused sub-bands. The performance of the proposed method is compared with fourteen state-of-the-art multi-focus image fusion methods using six objective quality metrics. Experimental results demonstrate that the proposed method is competitive with the state-of-the-art methods in terms of both subjective and objective assessments.

Perceptual quality assessment for multimodal medical image fusion

Recent years have witnessed that the multimodal medical image fusion (MMIF) plays critical roles in clinical diagnostics and treatment. Many MMIF algorithms have been proposed to improve the MMIF images quality. The quality of multimodal medical fused images will significantly affect the results of the clinical diagnosis. However, little work has been designed to evaluate the effectiveness of MMIF algorithms and the quality of MMIF images. To this end, this paper presents a perceptual quality assessment method for MMIF. A MMIF image database (MMIFID) is first built to employ the classical MMIF algorithms, and the subjective experiment is conducted to assess the quality of each fused image. Then, a no-reference objective method is proposed for the perceptual quality evaluation of MMIF images,which uses Pulse Coupled Neural Network (PCNN) in Non-subsampled Contourlet Transform (NSCT). A fused image is decomposed by NSCT into low frequency sub-band (LFS) and high frequency sub-band (HFS). It is used to motivate the PCNN processing, and large firing times are employed to measure LFS and HFS. Finally, two components evaluation results are combined to obtain the overall objective quality score. Experimental results based on the MMIFID indicate that our presented method outperforms the existing image fusion quality evaluation metrics, and it provides a satisfactory correlation with subjective scores, which shows effectiveness in the quality assessment of medical fused images.

Pan-sharpening via multi-scale and multiple deep neural networks

Interpreting remote sensing images by combining manual visual interpretation and computer automatic classification and recognition is an important application of human–computer interaction (HCI) in the field of remote sensing. Remote sensing images with high spatial resolution and high spectral resolution is an important basis for automatic classification and recognition. However, such images are often difficult to obtain directly. In order to solve the problem, a novel pan-sharpening method via multi-scale and multiple deep neural networks is presented. First, the non-subsampled contourlet transform (NSCT) is employed to decompose the high resolution (HR)/low resolution (LR) panchromatic (PAN) images into the high frequency (HF)/low frequency (LF) images, respectively. For pan-sharpening, the training sets are only sampled from the HF images. Then, the DNN is utilized to learn the feature of the HF images in different directions of HR/LR PAN images, which is trained by the image patch pair sampled from HF images of HR/LR PAN images. Moreover, in the fusion stage, NSCT is also employed to decompose the principal component of initially amplified LR multispectral (MS) image obtained by the transformation of adaptive PCA (A-PCA). The HF image patches of LR MS, as the input data of the trained DNN, go through forward propagation to obtain the output HR MS image. Finally, the output HF sub-band images and the original LF sub-band images of LR MS image fuse into a new sub-band set. The inverse transformations of NSCT and A-PCA , residual compensation are conducted to obtain the pan-sharpened HR MS. The experimental results show that our method is better than other well-known pan-sharpening methods.

Ferrite Magnetic Tile Defects Detection Based on Nonsubsampled Contourlet Transform and Texture Feature Measurement

The ferrite magnetic tiles are widely used in industry field. At present, the defects detection in ferrite magnetic tile surfaces is done by manual work. In order to improve the defects detection efficiency and prevent missed and false detection, an automatic detection system applied to the magnetic tiles non-destructive detection was proposed based on computer vision. A suit of the automatic defects detection equipment used for magnetic tile surfaces was designed so that we can adjust the position and angle of the lightings and cameras programmatically to meet the requirement of different location for different kinds of magnetic tiles. To solve the problem of automatically detect defects from magnetic tile images which are with dark colors and low contrasts, a new hybrid algorithm which combines nonsubsampled Contourlet transform and Laws texture feature measurement was proposed to eliminate the influence of the grinding textures and extract defects. In this methodology the original image was first decomposed by nonsubsampled Contourlet transform, the characteristics of the decomposition coefficients are analyzed by Laws texture feature measurement. Then according to the texture energies of the restructured image, a segmentation threshold was determined to reset the decomposition coefficients. Finally the image was reconstructed with the reconstruction coefficients, the grinding textures were eliminated and defects were obtained by Canny operator. The experimental results show that based on the proposed method, the grinding textures can be eliminated effectively, the defects can be extracted accurately, and the accuracy rate of extraction defects can achieve 93.57%. The automatic detection system can provide an effective solution to magnetic tile defects detection industry.

Improved contourlet transform construction and its application to surface defect recognition of metals

A new multiscale subspace method of feature extraction based on improved contourlet transform (ICT) and kernel spectral regression (KSR) is proposed. We construct the ICT according to the construction mode of contourlet transform and nonsubsampled contourlet transform (NSCT), ICI is built upon iterated nonsubsampled pyramids and subsampled directional filter banks to obtain directional multiresolution image representation. The ICT not only has the advantages of multi-resolution and fast calculation speed of contourlet transform but also has the advantages of low aliasing in the frequency domain of NSCT. KSR is a subspace learning method used to fast dimensionality reduction for multi-scale feature extracted. ICT–KSR, as a new feature extraction method, is applied to the inspection of metal surface defects in aluminum sheets and continuous casting slabs. The experimental results show that the proposed method performs better than the other methods. The best recognition rate of aluminum sheets and continuous casting slabs are up to 94.58% and 94.76%, respectively.

Discrete Wavelet Transform and Bird Swarm Optimized Bayesian Multimodal Medical Image Fusion

A novel Bayesian image fusion scheme using bird swarm optimization algorithm (BSA) is being proposed here.The medical Image fusion is progressed using the MRI brain images taken from the BRATS database and thesource images of different modalities are fused effectively to present an information rich fused image. The sourceimages are subjected to the Haar discrete wavelet transform (DWT) and the Bayesian fusion is performed usingthe Bayesian parameter, which is determined optimally using the BSA optimization. The analysis reveals that themethod outperformed the three existing methods of fusion that is nonsubsampled contourlet transform (NSCT),cascaded static wavelet (SWT) and NSCT ,that is (SWT-NSCT) and Holoentropy and SP-Whale Optimizationmethod (HW Fusion) with improved values of mutual information(1.4764),peak signal-to-noise ratio (37.2114)and root mean square error (9.9341) .

Image fusion with structural saliency measure and content adaptive consistency verification

Image fusion obtains a desired image by integrating the useful information of multiple input images. Most traditional fusion strategy is usually guided by image local contrast or variance, which cannot well represent visual discernable features of source images. And the undesirable seam effects or artifacts produced due to the inconsistency between fusion weight map and image content may severely degrade the visual quality of the fused images. An efficient image fusion method with structural saliency measure and content adaptive consistency verification was proposed. The fusion is implemented under the nonsubsampled contourlet transform (NSCT)-based image fusion framework. The low-frequency NSCT decomposition coefficients are fused with the weight map constructed by considering both structural saliency and visual uniqueness features and refined by spatial consistency with guide filter. The high-frequency NSCT decomposition coefficients are fused with structural saliency. The performances of the proposed method have been verified on several pairs of multifocus images, infrared-visible images, and multimodal medical images. Experimental results clearly demonstrate the superiority of the proposed algorithm compared with several existing state-of-the-art algorithms in terms of both visual and quantitative comparison.

Side-Scan Sonar Image Fusion Based on Sum-Modified Laplacian Energy Filtering and Improved Dual-Channel Impulse Neural Network

The operation mode of a single strip provides incomplete side-scan sonar image in a specific environment and range, resulting in the overlapping area between adjacent strips often with imperfect detection information or inaccurate target contour. In this paper, a sum-modified Laplacian energy filtering (SMLF) and improved dual-channel pulse coupled neural network (IDPCNN) are proposed for image fusion of side-scan sonar in the domain of nonsubsampled contourlet transform (NSCT). Among them, SMLF energy is applied to extract the fusion coefficients of the low frequency sub-band, which combines the characteristics of energy information, human visual contrast, and guided filtering to eliminate the pseudo contour effect of block flow. In addition, the IDPCNN model, which utilizes the average gradient, soft limit function, and novel sum-modified Laplacian (NSML) to adaptively represent the corresponding excitation parameters, is applied to improve the depth and activity of pulse ignition, so as to quickly and accurately select the image coefficients of the high frequency sub-band. The experimental results show that the proposed method displays fine geomorphic information and clear target contour in the overlapping area of adjacent strips. The objective index values are generally optimal, which reflect the information of image edge, clarity, and overall similarity.

Remote sensing image enhancement based on the combination of adaptive nonlinear gain and the PLIP model in the NSST domain

To enhance image detail and contrast effectively, we present a novel enhancement method for remotely sensed images. This method is based on the combination of adaptive nonlinear gain and the parameterized logarithmic image processing model (PLIP) in the nonsubsampled shearlet transform (NSST) domain. The algorithm works in several stages by deconstructing the image into low- and high-frequency components, applying different functions to each set of frequency components, and then applying further enhancement functions to the reconstructed image. The experimental results show that the proposed method performs well in terms of definition gain, the contrast improvement index (CII) and the measure of enhancement by entropy (EMEE) when compared to several state-of-the-art image enhancement algorithms, including the nonsubsampled contourlet transform (NSCT) with fuzzy field enhancement, the NSCT with unsharp masking, the feature-linking model, linking synaptic computation for image enhancement and improved fuzzy contrast in the NSST domain.

Implementation Of Spatial-Scale Domain Based De-Noising Techniques using Different Thresholding

This paper aims in presenting a thorough comparison of performance and usefulness of multi-resolution based de-noising technique. Multi-resolution based image de-noising techniques overcome the limitation of Fourier, spatial, as well as, purely frequency based techniques, as it provides the information of 2-Dimensional (2-D) signal at different levels and scales, which is desirable for image de-noising. The multi-resolution based de-noising techniques, namely, Contourlet Transform (CT), Non Sub-sampled Contourlet Transform (NSCT), Stationary Wavelet Transform (SWT) and Discrete Wavelet Transform (DWT), have been selected for the de-noising of camera images. Further, the performance of different de-nosing techniques have been compared in terms of different noise variances, thresholding techniques and by using well defined metrics, such as Peak Signal-to-Noise Ratio (PSNR) and Root Mean Square Error (RMSE). Analysis of result shows that shift-invariant NSCT technique outperforms the CT, SWT and DWT based de-noising techniques in terms of qualititaive and quantitative objective evaluation.

Dynamic Histogram Equalization for contrast enhancement for digital images

Magnetic Resonance Imaging (MRI) is an efficient tool, produced by applying radio waves and magnetic fields which is being useful in the diagnosis of various diseases like cancer, epilepsy and stroke etc. The quality of the resulting image is needed to be enhanced because it is challenging for the specialists to investigate. Modified Histogram Equalization on Fuzzy based Improved Particle Swarm Optimization (FIPSO) is proposed for Dynamic Histogram Equalization which resolves this problem through image contrast enhancement. The details of an images are captured by smoothing and it uses Gaussian function to distribute pixel intensity to nearest pixel. It uses normal distribution and here blur is removed by applying Non subsampled Contourlet Transform. Then local maxima are calculated to extract dark and bright pixel values. The smoothed images are fuzzified with TSK (Takagi-Sugeno-Kang) model and it provides importance to all the local maxima intervals. An Improved particle swarm optimization (IPSO) algorithm is obtained by combining Galactic Swarm Optimization (GSO) with PSO which equalizes histogram of an image. FIPSO algorithm is used to the minimum contrast images of MRI brain images. Non-subsampled Contourlet transform (NSCT) based modified histogram equalization enhances image contrast. Here IPSO generates optimum values and these value are used to calculate cumulative distribution function in histogram equalization. The quality measures demonstrate that the current equalization technique attains highest performance against existing techniques in terms of brightness and contrast.

Multi-modality medical image fusion based on separable dictionary learning and Gabor filtering

Sparse representation (SR) has been widely used in image fusion in recent years. However, source image, segmented into vectors, reduces correlation and structural information of texture with conventional SR methods, and extracting texture with the sliding window technology is more likely to cause spatial inconsistency in flat regions of multi-modality medical fusion image. To solve these problems, a novel fusion method that combines separable dictionary optimization with Gabor filter in non-subsampled contourlet transform (NSCT) domain is proposed. Firstly, source images are decomposed into high frequency (HF) and low frequency (LF) components by NSCT. Then the HF components are reconstructed sparsely by separable dictionaries with iterative updating sparse coding and dictionary training. In the process, sparse coefficients and separable dictionaries are updated by orthogonal matching pursuit (OMP) and manifold-based conjugate gradient method, respectively. Meanwhile, the Gabor energy as weighting factor is utilized to guide the LF components fusion, and this further improves the fusion degree of low-significant feature in the flat regions. Finally, the fusion components are transformed to obtain fusion image by inverse NSCT. Experimental results demonstrate the more competitive results of the proposal, leading to the state-of-art performance on both visual quality and objective assessment.

NVST chromosphere data interference fringes removal based on NSCT and PCA

Abstract The New Vacuum Solar Telescope (NVST) has been affected by thin film interference since its operation, which makes interference fringes superimposed on its sun chromosphere data products. These fringes have a serious impact on the quantitative analysis and high-resolution reconstruction of solar fine structure observation. Although some methods have been adopted to remove the interference fringes in NVST chromosphere data, most of them are performed on high-resolution reconstructed data, and little for short-exposure data (speckle images). The short-exposure data are the raw data of the NVST observation. If the fringes on the data can be eliminated or weakened, the quality of subsequent high-resolution reconstructed data will be improved. Therefore, this paper proposed a method to remove the interference fringes in short-exposure data based on Non-Subsampled Contourlet Transform (NSCT) and Principal Component Analysis (PCA). First, NSCT multi-scale decomposition is utilized to divide the data into fringe-containing scales and fringe-free scales, and then the fringes are removed by PCA in fringe-containing scales. Experiments show that the interference fringes in the data can be removed effectively while preserving the solar structures of NVST data.

Palm vein recognition system based on multi-block statistical features encoding by phase response information of nonsubsampled contourlet transform

In this paper, we improve our palm vein recognition system to be based on phase response information of nonsubsampled contourlet transform (NSCT). First, we localise the region of interest (ROI), next, we have divided the ROI into a non-overlapping block and we proposed an encoding method based on extracting phase response information of NSCT coefficients, then XOR pattern is applied to extract invariant from local region of the palm vein to create a palm vein template of 512 bytes. Finally, we have calculated the modified hamming distance between templates to estimate the similarity between two palm veins filtered images. The method is tested on the CASIA Multispectral Palmprint Database. The experimental results illustrate the effectiveness of this coding in two modes of biometric palm vein: 99.90% of rank-one recognition rate and 0.19% of equal error rate in verification.