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Full-field optical angiography (FFOA)--a real-time non-invasive imaging technique for extracting biological blood microcirculation information--contributes to an in-depth understanding of the functional and pathological changes of biological tissues. However, owing to the limitation of the depth-of-field (DOF) of optical lenses, existing FFOA imaging methods cannot capture an image containing every blood-flow information. To address this problem, this study develops a long-DOF full-field optical angiography imaging system and proposes a novel multi-focus image fusion scheme to expand the DOF. First, FFOA images with different focal lengths are acquired by the absorption intensity fluctuation modulation effect. Second, an image fusion scheme based on gradient feature detection in a nonsubsampled contourlet transform domain is developed to capture focus features from FFOA images and synthesize an all-focused image. Specifically, FFOA images are decomposed by NSCT into coefficients and low-frequency difference images; thereafter, two gradient feature detection-based fusion rules are used to select the pre-fused coefficients. The experimental results of both phantom and animal cases show that the proposed fusion method can effectively extend the DOF and address practical FFOA image defocusing problems. The fused FFOA image can provide a more comprehensive description of blood information than a single FFOA image. [ABSTRACT FROM AUTHOR]

Foggy images suffer from severe halo disturbances, low brightness and low contrast, etc. In this paper, to solve these problems, we propose a foggy image defogging method. First, the nonSubsampled contour transform (NSCT) is employed in this method to partition the image into low and high frequency elements. In order to enhance the preservation of complex information, the Retinex approach utilises a bootstrap filter instead of a Gaussian filter. The denoising method entails the application of a threshold to the high-frequency sub-band pictures. This involves boosting coefficients that surpass the threshold and attenuating coefficients that are below it. The ultimate image is recreated following dehazing by employing the inverse transformation of NSCT. Then, the grey wolf optimization (GWO) algorithm is employed to optimise the regularisation factor of the kernel in the guided filter, hence improving the defogging process. Finally, through extensive experiments, our proposed method outperforms recent approachs under plenty of metrics in terms of visual quality. The method effectively reduces halo aberrations, preserves intricate details, and significantly enhances picture sharpness by reducing atmospheric haze. [ABSTRACT FROM AUTHOR]

Medical imaging has played an essential role in medicine and disease diagnosis. Medical images from a single modality contain limited data about the organ. On the other hand, images from different modalities contain valuable structural and functio nal data about an organ. Medical image fusion (MIF) strategies integrate complementary information from two medical images captured using distinct modalities. This paper offered a new multimodal MIF approach using the parameter-adaptive pulse-coupled neural networks (PA-PCNN) within the non-subsampled contourlet transform (NSCT). The NSCT decomposes those images into high- and low-frequency bands. PA-PCNN combines those bands. The fused image was created using the inverse of the NSCT approach. To prove the proposed approach's performance, we appoint a variety of medical images like computed tomography (CT), magnetic resonance imaging (MRI), single-photon emission CT (SPECT), and positron emission tomography (PET). Our experiments use five fusion metrics to validate the proposed approach's performance, such as entropy (EN), mutual information (MI), weighted edge information (Q A B / F ), nonlinear correlation information entropy (Q ncie ), and average gradient (AG). Outcomes show that the proposed approach achieves high overall performance in visual and objective characteristics when compared with five well-known MIF methods. The average values for EN, MI, Q A B / F , Q ncie , and AG with the proposed approach are 5.2144,3.1282,.6600,.8071, and 8.9874, respectively. [ABSTRACT FROM AUTHOR]

Information regarding single-modality medical images remains limited at present. Therefore, the details of relevant organizations cannot be reflected by such information and misdiagnosis may result in the clinical setting. A novel image fusion method based on nonsubsampled contourlet transform (NSCT) and visual saliency is presented for multimode medical images to solve the aforementioned problem and improve the effect of fused images. In this method, an image is initially decomposed via NSCT to obtain low- and high-frequency coefficients and derive the Achanta (AC) saliency map of the image. Subsequently, the similarity of low- and high-frequency subbands is calculated using the standard deviation and energy of a region. Similarity is determined through an adaptive threshold. Meanwhile, the saliency map is utilized to guide the fusion of high- and low-frequency coefficients, with emphasis on the visual salient target area of the source image. Lastly, inverse NSCT is used to reconstruct the fused image. The proposed algorithm can effectively improve the quality of multimodal medical image fusion and increase complementary information among different modalities as demonstrated by the experimental results. This algorithm is superior to existing medical image fusion algorithms and more in accord with human visual characters. [ABSTRACT FROM AUTHOR]

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In radiography, an antiscatter grid is a well-known device for eliminating unexpected x-ray scatter. We investigate a new stationary grid artifact suppression method based on a nonsubsampled contourlet transform (NSCT) incorporated with Gaussian band-pass filtering. The proposed method has an advantage that extracts the Moiré components while minimizing the loss of image information and apply the prior information of Moiré component positions in multi-decomposition sub-band images. We implemented the proposed algorithm and performed a simulation and an experiment to demonstrate its viability. We did this experiment using an x-ray tube (M-113T, Varian, focal spot size: 0.1 mm), a flat-panel detector (ROSE-M Sensor, Aspenstate, pixel dimension: 3032 × 3800 pixels, pixel size: 0.076 mm), and carbon graphite-interspaced grids (JPI Healthcare, 18 cm × 24 cm, line density: 103 LP/inch and 150 LP/inch, ratio: 5:1, focal distance: 65 cm). Our results indicate that the proposed method successfully suppressed grid artifacts by reducing them without either reducing the spatial resolution or causing negative side effects. Consequently, we anticipate that the proposed method can improve image acquisition in a stationary grid x-ray system as well as in extended x-ray imaging.

Full-field optical angiography (FFOA) has considerable potential for clinical applications in the prevention and diagnosis of various diseases. However, owing to the limited depth of focus attainable using optical lenses, only information about blood flow in the plane within the depth of field can be acquired using existing FFOA imaging techniques, resulting in partially unclear images. To produce fully focused FFOA images, an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency is proposed. Firstly, an imaging system is constructed, and the FFOA images are acquired by intensity-fluctuation modulation effect. Secondly, we decompose the source images into low-pass and bandpass images by performing nonsubsampled contourlet transform. A sparse representation-based rule is introduced to fuse the lowpass images to effectively retain the useful energy information. Meanwhile, a contrast spatial frequency rule is proposed to fuse bandpass images, which considers the neighborhood correlation and gradient relationships of pixels. Finally, the fully focused image is produced by reconstruction. The proposed method significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets. Experimental results confirm that the proposed method outperformed some state-of-the-art methods in both qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]

Combining two medical images from different modalities is more helpful for using the resulting image in the healthcare field. Medical image fusion means combining two or more images coming from multiple sensors. This technology obtains an output image that presents more effective and useful information from two images. This paper proposes a multi-modal medical image fusion algorithm based on the nonsubsampled contourlet transform (NSCT) and pulse coupled neural networks (PCNN) methods. The input images are decomposed using the NSCT method into low- and high-frequency subbands. The PCNN is a fusion rule for integrating both low- and high-frequency subbands. The inverse of the NSCT method is to reconstruct the fused image. The results of medical image fusion help doctors with disease diagnosis and patient treatment. The proposed algorithm is tested on six groups of multi-modal medical images using 100 pairs of input images. The proposed algorithm is compared with eight fusion methods. We evaluate the performance of the proposed algorithm using the fusion metrics: peak signal to noise ratio (PSNR), mutual information (MI), entropy (EN), weighted edge information (Q A B / F ), nonlinear correlation information entropy (Q ncie ), standard deviation (SD), and average gradient (AG). Experimental results show that the proposed algorithm can perform better than other medical image fusion methods and achieve promising results. [ABSTRACT FROM AUTHOR]

Heterologous image fusion has been widely used to integrate multiple images into one. The fused images also present a higher definition, more significant edge intensity, and more information than the source image. There are different characteristics of image data collected by the various types of sensors. Among them, the depth sensor imaging used the Time of Flight (ToF) to realize the distance calculation using the ToF near-infrared light. The beneficial supplement has been commonly used for the visible light camera. The broad application can also be expected in the agriculture, medical treatment, quality inspection, and vision fields. However, the image acquisition of a single natural scene cannot fully meet the requirements for rapid and accurate identification of the fruits and positioning targets. The image fusion can be extended to the heterologous vision system using multi-objective optimization, particularly in the field of natural scenes. In this study, a multi-scale decomposition and dual optimization strategy was proposed to simplify the ToF and visible-light image fusion in an apple orchard using the Simplified Pulse Coupled Neural Network (SPCNN). A double strategy with parameter optimization was introduced into the SPCNN model for the fusion of Nonsubsampled Contourlet Transform (NSCT). The model included the registration module, coding area, multi-scale decomposition module, single target SPCNN fusion model, multi-target SPCNN fusion model, and decoding area. The heterologous vision system was also used to accurately register the ToF and visible light images. Four parameters of SPCNN model were encoded, including the link channel feedback term, link strength, dynamic threshold attenuation factor, and dynamic threshold amplification factor. The NSCT was used to decompose the image at multiple scales. The fusion rules in the SPCNN model were adopted with the improved artificial bee colony algorithm and double optimization, including the single- and multi-objective parameter optimization. Each binary vector was converted into the real parameters using the decoding area. The objective function of the double optimization was used as the iteration termination of the SPCNN model. Finally, the heterogeneous image fusion was implemented after the multi-scale inverse transformation. There was improved parameter optimization and iteration times of SPCNN model. The adaptive ignition times of the model were relatively low (about 3-7 times), indicating low ignition times, adaptive segmentation, and high efficiency. The success rate of ignition recognition reached 100.00%, and the minimum duration of ignition division reached 91.91 s at 15:00. Specifically, the success rate of fusion image recognition also reached 100.00% under different periods, including strong, medium, and weak light at 12:00, 15:00, 18:20, and 19:00, compared with the rest fusion models. The fusion time was much lower than that of SPCNN model, with a minimum of 92.68 s. The four fusion indexes were the largest in the weak light period of 18:20, including the average gradient, correlation coefficient, mutual information, entropy, and spatial frequency. The proposed model presented an excellent performance in accuracy, time-consuming, and model size. The finding can provide a supplement to the image hierarchical fusion. [ABSTRACT FROM AUTHOR]

Full-field optical angiography (FFOA) has considerable potential for clinical applications in the prevention and diagnosis of various diseases. However, owing to the limited depth of focus attainable using optical lenses, only information about blood flow in the plane within the depth of field can be acquired using existing FFOA imaging techniques, resulting in partially unclear images. To produce fully focused FFOA images, an FFOA image fusion method based on the nonsubsampled contourlet transform and contrast spatial frequency is proposed. Firstly, an imaging system is constructed, and the FFOA images are acquired by intensity-fluctuation modulation effect. Secondly, we decompose the source images into low-pass and bandpass images by performing nonsubsampled contourlet transform. A sparse representation-based rule is introduced to fuse the lowpass images to effectively retain the useful energy information. Meanwhile, a contrast spatial frequency rule is proposed to fuse bandpass images, which considers the neighborhood correlation and gradient relationships of pixels. Finally, the fully focused image is produced by reconstruction. The proposed method significantly expands the range of focus of optical angiography and can be effectively extended to public multi-focused datasets. Experimental results confirm that the proposed method outperformed some state-of-the-art methods in both qualitative and quantitative evaluations.

Disadvantages of deep convolutional neural networks (CNNs) in image super-resolution (SR) are slow convergence rate, long training time, slow run time, complexity and high computational cost. On the other hand, shallow CNNs, while have lack of above-mentioned disadvantages for deep CNNs, do not have ability of learning the high frequency components appropriately. The purpose of this paper is promoting shallow networks so as to increase learning ability for fine details of the image while keeping the previous advantages. We present a learning-based approach which is composition of a feature extraction transformation unit with a training and approximation unit (shallow CNNs). In the transformation unit, to extract fine details of the image, nonsubsampled contourlet transform (NSCT) is used. Transforming the feature space to frequency domain causes that the output coefficients to have enough sparse so that by increasing the sparse coefficients, training of neural network will be easier and the training time will decrease considerably. Advantages of the proposed approach with respect to other approaches are high reconstruction accuracy, high training speed, low runtime, lower network complexity and faster convergence. [ABSTRACT FROM AUTHOR]

In order to avoid the noise diffusion and amplification caused by traditional dehazing algorithms, a single image haze removal algorithm based on nonsubsampled contourlet transform (HRNSCT) is proposed. The HRNSCT removes haze only from the low-frequency components and suppresses noise in the high-frequency components of hazy images, preventing noise amplification caused by traditional dehazing algorithms. First, the nonsubsampled contourlet transform (NSCT) is used to decompose each channel of a hazy and noisy color image into low-frequency sub-band and high-frequency direction sub-bands. Second, according to the low-frequency sub-bands of the three channels, the color attenuation prior and dark channel prior are combined to estimate the transmission map, and use the transmission map to dehaze the low frequency sub-bands. Then, to achieve the noise suppression and details enhancement of the dehazed image, the high-frequency direction sub-bands of the three channels are shrunk, and those shrunk sub-bands are enhanced according to the transmission map. Finally, the nonsubsampled contourlet inverse transform is performed on the dehazed low-frequency sub-bands and enhanced high-frequency sub-bands to reconstruct the dehazed and noise-suppressed image. The experimental results show that the HRNSCT provides excellent haze removal and noise suppression performance and prevents noise amplification during dehazing, making it well suited for removing haze from noisy images.

Although the traditional image fusion method can obtain rich image results, obvious artificial noise and artifacts are often present in the resulting image. Fusion algorithms based on neural networks can avoid the shortcomings of traditional methods, but they are more complex and less flexible. In this study, we proposed a fusion method using the deep residual neural network ResNet152, which can not only effectively suppress artificial noise but also preserve the edge details of the image and improve the efficiency of the neural network. The proposed method is characterized by a multiscale transformation of an infrared image and visible light image in the optimized nonsubsampled contourlet transformation domain, and the deep residual neural network ResNet152 is used to extract the deep features of the low-pass component to guide the fusion of the low-pass component. The bandpass component is fused by taking the modulus maximum. This method can fully retain the global features and structural information of the source image in the result image. Compared to existing fusion methods using public test image sets, the experimental results show that on a subjective level, the fusion method creates sharper depth edges and fewer noise artifacts than traditional fusion methods. From an objective perspective, the average value for the results of the evaluation function is greater than that of other fusion methods.

In this paper we propose the FD-TR: Feature Detector Based on Scale Invariant Feature Transform and Bidirectional Feature Regionalization for digital image watermarking. The Scale Invariant Feature Transform method is applied to extract keypoints and an Edge and Neighbor Filtering method is proposed to generate the candidate feature points. Then the Bidirectional Feature Regionalization method is proposed and applied in order to classify candidate feature points and form candidate feature regions. On this basis, the Candidate Feature Region Filtering method is proposed to select the final feature regions for watermarking. During the watermarking process, the Nonsubsampled Contourlet Transform is employed to the extracted feature regions to extract the low-frequency coefficients. Next, we use the Diagonal Matrix-based Spread Transform Dither Modulation for watermark embedding and extraction. Extensive experiments have been conducted to evaluate the performance of the proposed scheme and the comparison with existing methods demonstrate that the proposed method is superior to the existing methods in terms of robustness and quality. [ABSTRACT FROM AUTHOR]

The aircraft environmental control system (AECS) plays a vital role in the normal operation of an airplane; thus, performance degradation of the AECS may result in failure and even catastrophic consequences. Because performance degradation is inevitable, performance degradation assessment is critical to provide information for appropriate maintenance actions. This paper proposed an integrated framework for performance degradation assessment of AECS by introducing the visual cognition theory into the specific method. First, an observer is used to generate a residual error, which contains degradation information of the AECS. Second, the generated residual error is transformed into a two-dimensional image based on a permutation method. Third, inspired by the multichannel characteristic (MCC), the transformed image is decomposed into several subbands based on the nonsubsampled contourlet transform (NCST) for multiscale and multidirectional feature extraction, thus capturing the precise characteristics of degradation. Then, inspired by the manifold sensing characteristic (MSC), the manifold space is established based on diffusion maps (DM) to reveal the intrinsic evolutionary law of health degradation. Finally, the geodesic distance is employed to represent the deviation between the current state and health baseline. As a health indicator, the confidence value (CV), which is derived from the geodesic distance, is capable of representing the degradation trend of the AECS. The whole process of degradation can be tracked in real time based on the proposed method. An AECS simulation model was established in MATLAB/Simulink, and two typical faults were introduced. The results based on simulation data demonstrate that the proposed assessment method can effectively reflect the degradation process of the AECS and the constructed health indicator is reasonable. [ABSTRACT FROM AUTHOR]

Lung Cancer is one among deadly disease since it is aggressive in nature as well as deferred detections at advanced stages. The survival rate due to lung cancer mainly relies on prompt detection which is regarded as major challenge. Former approaches such as iW-Net based segmentation and Enhanced Inception-Residual Convolutional Neural Network (EIRCNN) based lung nodule detection are utilized for lung cancer detection. Though traditional iW-Net architecture outperforms in an improved way for CT medical images with challenging datasets through extensive experimentations, certain aspects are lagging pertaining to architecture. Yet further examination is required for optimal features selection in Lung nodule detection. To mitigate these issues, combination of Improved Stacked Autoencoder and Enhanced Pre-Activation Residual Convolutional Neural Network (EPARCNN) is greatly utilized for pulmonary nodule detection. Also robust image enhancement in lung CT and X-ray is achieved by suggesting Fuzzy Normalized Gamma-Corrected Contrast-Limited Adaptive Histogram Equalization (FNGCCLAHE) with Nonsubsampled Contourlet Transform (NSCT) and segmentation through Sub-Intensity range-based Pulse-Coupled Neural Network (SIPCNN). LIDC-IDRI and X-ray image datasets are utilized for assessing suggested CoISAE-PARCNN system with respect to factors such as precision, recall, f-measure, Peak Signal-o-Noise Ratio (PSNR), time, error rate and accuracy. It is thereby validated through experimental outcomes that the proposed system outperforms well in contrary to various prevailing approaches like LOG, Fast-RCNN, EWRCNN and EIRCNN. [ABSTRACT FROM AUTHOR]

The image fusion method based on sparse representation in the single‐scale image domain has produced better fusion results than the classic methods based on multi‐scale analysis nowadays. However, due to the limited number of dictionary atoms, it is difficult to provide an accurate description for image details in the sparse‐representation‐based image fusion methods, and it requires a lot of time. A novel dictionary is constructed with non‐subsampled contourlet transform and sparse representation by using the proposed simultaneous strategy. Then the novel dictionary could combine the sparsity attribute of the learning dictionary with a multi‐scale feature of non‐subsampled contourlet transform. Moreover, the simultaneous strategy is combined with this novel dictionary so that sparse coefficients can be represented with the same dictionary atoms and thus they can be compared in a reasonable and accurate way. Finally, the image fusion method along with this novel dictionary is proposed and named non‐subsampled contourlet transform (NSCT)–simultaneous sparse representation (SSR). Experimental results show that the proposed fusion method NSCT–SSR, with its more excellent fusion effect and better anti‐noise capability, outperforms the existing fusion methods, which are based on both multi‐scale domain and sparse representation in the single‐scale image domain.

Remote sensing image fusion is very important for playing the advantages of a variety of remote sensing data. However, remote sensing image fusion is large in computing capacity and time consuming. In this paper, in order to fuse remote sensing images accurately and quickly, a parallel fusion algorithm of remote sensing image based on NSCT (nonsubsampled contourlet transform) is proposed. In the method, two important kinds of remote sensing image, multispectral image and panchromatic image are used, and the advantages of parallel computing in high performance computing and the advantages of NSCT in information processing are combined. In the method, based on parallel computing, some processes with large amount of calculation including IHS (Intensity, Hue, Saturation) transform, NSCT, inverse NSCT, inverse IHS transform, etc., are done. To realize the method, multispectral image is processed with IHS transform, and the three components, I, H, and S are gotten. The component I and the panchromatic image are decomposed with NSCT. The obtained low frequency components of NSCT are fused with the fusion rule based on the neighborhood energy feature matching, and the obtained high frequency components are fused with the fusion rule based on the subregion variance. Then the low frequency components and the high frequency components after fusion are processed with the inverse NSCT, and the fused component is gotten. Finally, the fused component, the component H and the component S are processed with the inverse IHS transform, and the fusion image is obtained. The experiment results show that the proposed method can get better fusion results and faster computing speed for multispectral image and panchromatic image.

Many types of medical images must be fused, as single‐modality medical images can only provide limited information due to the imaging principles and the complexity of human organ structures. In this paper, a multimodal medical image fusion method that combines the advantages of nonsubsampling contourlet transform (NSCT) and fuzzy entropy is proposed to provide a basis for clinical diagnosis and improve the accuracy of target recognition and the quality of fused images. An image is initially decomposed into low‐ and high‐frequency subbands through NSCT. The corresponding fusion rules are adopted in accordance with the different characteristics of the low‐ and high‐frequency components. The membership degree of low‐frequency coefficients is calculated. The fuzzy entropy is also computed and subsequently used to guide the fusion of coefficients to preserve image details. High‐frequency components are fused by maximizing the regional energy. The final fused image is obtained by inverse transformation. Experimental results show that the proposed method achieves good fusion effect based on the subjective visual effect and objective evaluation criteria. This method can also obtain high average gradient, SD, and edge preservation and effectively retain the details of the fused image. The results of the proposed algorithm can provide effective reference for doctors to assess patient condition. [ABSTRACT FROM AUTHOR]

This article proposes an improved partial differential equation (PDE)-based total variation (TV) model that enhances grey and coloured brain tumour images obtained by magnetic resonance imaging. A nonsubsampled contourlet transform was applied to images from standard databases that converted into lowpass and highpass (or bandpass) contourlet coefficients. An improved version of the power-law transform method was used on the lowpass contourlet coefficients, and an adaptive threshold method was applied to the highpass (or bandpass) contourlet coefficients. The inverse contourlet transform was performed on all the enhanced contourlet coefficients to generate a complete brain tumour image. Finally, the PDE-based TV model was applied to this image to produce the denoised image. The performance of the suggested method was calculated in terms of the peak signal-to-noise ratio, mean square error, and structural similarity index. This method achieved the best peak signal-to-noise ratio, mean square error, and structural similarity index of 77.9846 dB, 0.00012612, and 97.895%, respectively, compared to the conventional PDE+modified transform-based gamma correction, adaptive PDE+generalized cross-validation, parallel magnetic resonance imaging, and Berkeley wavelet transform+support vector machine methods. [ABSTRACT FROM AUTHOR]

Morphological networks constructed with structural magnetic resonance imaging (sMRI) images have been widely investigated by exploring interregional alterations of different brain regions of interest (ROI) in the spatial domain for Alzheimer's disease (AD) classification. However, few attentions are attracted to construct a subband-based individual network with the sMRI image in the frequency domain. In order to verify the feasibility of constructing individual networks with subbands and extract features from the subband-based individual network for AD classification, in this study, we propose a novel method to capture correlations of the abnormal energy distribution patterns related to AD by constructing nonsubsampled contourlet subband-based individual networks (NCSINs) in the frequency domain. Specifically, a 2-dimensional representation of the preprocessed sMRI image is firstly reshaped by downsampling and reconstruction steps. Then, the nonsubsampled contourlet transform is performed on the 2-dimensional representation to obtain directional subbands, and each directional subband at one scale is described by a column energy feature vector (CV) regarded as a node of the NCSIN. Subsequently, edge between any two nodes is weighted with connection strength (CS). Finally, the concatenation of node and edge features of the NCSINs at different scales is used as a network feature of the sMRI image for AD classification. Meanwhile, the support vector machine (SVM) classifier with a radial basis function (RBF) kernel is applied for categorizing 680 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Experimental results demonstrate that it is feasible to construct the subband-based individual network in the frequency domain and also show that our NCSIN method outperforms five other state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

The multi-source image fusion has been a hot topic during recent years because of its higher segmentation accuracy rate. However, the traditional multi-source image fusion methods could not obtain better contrast and more details of the fused image. To better detect the pig-body feature, a novel infrared and visible image fusion method for pig-body segmentation and temperature detection is proposed in non-subsampled contourlet transform (NSCT) domain, named as NSCT-GF. Firstly, the visible and infrared images were decomposed into a series of multi-scale and multi-directional sub-bands using NSCT. Then, to better represent the fine-scale of texture information, the Gabor energy map was extracted by Gabor filter with even-symmetry, and the low-frequency coefficients were fused by the maximum of Ordinal encoding. Then, to preserve the more coarse-scale and edge detail information, Gabor filter with odd-symmetry was employed to fuse high-frequency NSCT sub-bands and the fused coefficients were reconstructed into a final fusion image by inverse NSCT. Next, the pig-body shape was obtained by Ostu automatic threshold segmentation and optimized by morphological processing. Finally, the pig-body temperature was extracted based on shape segmentation. Experimental results showed that the proposed segmentation method was capable of achieving 1.84–3.89% higher average segmentation accuracy rate than the prevailing conventional methods and also improved efficiency in terms of time consumption. It lays a foundation for accurately measuring the temperature of pig-body. [ABSTRACT FROM AUTHOR]

To design an efficient partial differential equation-based total variation method for denoising and possibilistic fuzzy c-means clustering algorithm for segmentation and these methods presented the more detailed information of the MRI medical images compared to traditional methods. In this article, the pipeline of the proposed method described by two modules like pre-processing and segmentation. In pre-processing, noisy image is decomposed using nonsubsampled contourlet transform and it contains highpass contourlet coefficient (i.e., noisy coefficient) is removed by the threshold method as well. After reconstruction, the primary denoised image is enhanced by an improved partial differential equation-based total variation method in terms of image details like edges, boundaries, etc. In segmentation, the enhanced primary denoised image is segmented by an improved possibilistic fuzzy c-means clustering algorithm that avoids limitations in possibilistic c-means, fuzzy c-means, and K-means clustering. Next, a support vector machine classifier is utilized to identify brain tissues into gray matter, white matter, cerebrospinal fluid, and tumor part. The parameters were optimally selected by a grey wolf optimization algorithm for the classification of brain tissues. The performance of the proposed method is computed with reference to peak signal-to-noise ratio, mean square error, structural similarity index, sensitivity, specificity, and accuracy. The experimental results claimed that the proposed method is better than the traditional methods. [ABSTRACT FROM AUTHOR]

Image fusion aims at aggregating the redundant and complementary information in multiple original images, the most challenging aspect is to design robust features and discriminant model, which enhances saliency information in the fused image. To address this issue, the authors develop a novel image fusion algorithm for preserving the invariant knowledge of the multimodal image. Specifically, they formulate a novel unified architecture based on non‐subsampled contourlet transform (NSCT). Their method introduces Quadtree decomposition and Bezier interpolation to extract crucial infrared features. Furthermore, they propose a saliency advertising phase congruency‐based rule and local Laplacian energy‐based rule for low‐ and high‐pass sub‐bands fusion, respectively. In this approach, the fusion image could not only combine the local and global features of the source image to avoid smoothing the edge of the target, but also retain the minor scales details and resists the interference noise of the multi‐modal image. Both objective assessments and subjective visions of experimental results indicate that the proposed algorithm performs competitively in both objective evaluation criteria and visual quality. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the research presented here, the novel neural network based watermarking framework is investigated in the area of transformation, while contourlet transform and nonsubsampled contourlet transform are realized to address the proposed idea via the Kurtosis to choose the band of suitable coefficients. It is to note that there are a number of techniques to deal with the aforementioned watermarking framework through the new integration of contourlet transform and nonsubsampled contourlet transform in connection with the perceptron neural network to extract the logo information, appropriately. There is the optimization technique through the genetic algorithm to provide the optimum results in the procedure of designing, as well. The approaches of the embedding and the de-embedding in case of learning algorithm of the neural network via individual training data set are considered in the present research to carry out a series of experiments with different scenario for the purpose of verifying the proposed techniques, obviously. [ABSTRACT FROM AUTHOR]

Binarization is the starting step of document analysis and recognition systems. A binarization method is proposed for a degraded historical document image. The binarization methodology is based on the joint use of nonsubsampled contourlet transform (NSCT) for enhancement and k-means clustering for binarization. The input degraded image is decomposed by NSCT for generating coefficients, which are handled through a weighting scheme for highlighting significant features. The resulting reconstructed enhanced image is then binarized by mapping pixels into foreground (text) or background (no text) using k-means clustering. Experiments are conducted on document image binarization competition datasets using blind and unblind evaluation protocol. Unblind evaluation is performed on four specific types of degradations, which are stain, ink bleed-through, nonuniform background, and ink intensity variation. The obtained results show the effectiveness of the proposed scheme in terms of objective and subjective evaluations as well as stability with respect to the other well-known methods. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a new framework for textured image retrieval, which is based on Weibull statistical distribution and nonsubsampled contourlet transform. Firstly, the image is decomposed into one lowpass subband and several highpass subbands by using nonsubsampled contourlet transform (NSCT). Secondly, Weibull probability distribution is employed to describe the statistical characteristics of the highpass NSCT coefficients, and the Weibull model parameters are utilized to construct a compact texture image feature space. Finally, image similarity measurement is accomplished by using closed-form solutions for the Kullback–Leibler divergences between the Weibull statistical models. Experimental results demonstrate the high efficiency of our textured image retrieval scheme, which can provide better retrieval rates and lower computational cost, in comparison with the state-of-the-art approaches recently proposed in the literature. [ABSTRACT FROM AUTHOR]

Multi-scale transforms (MST)-based methods are popular for multi-focus image fusion recently because of the superior performances, such as the fused image containing more details of edges and textures. However, most of MST-based methods are based on pixel operations, which require a large amount of data processing. Moreover, different fusion strategies cannot completely preserve the clear pixels within the focused area of the source image to obtain the fusion image. To solve these problems, this paper proposes a novel image fusion method based on focus-region-level partition and pulse-coupled neural network (PCNN) in nonsubsampled contourlet transform (NSCT) domain. A clarity evaluation function is constructed to measure which regions in the source image are focused. By removing the focused regions from the source images, the non-focus regions which contain the edge pixels of the focused regions are obtained. Next, the non-focus regions are decomposed into a series of subimages using NSCT, and subimages are fused using different strategies to obtain the fused non-focus regions. Eventually, the fused result is obtained by fusing the focused regions and the fused non-focus regions. Experimental results show that the proposed fusion scheme can retain more clear pixels of two source images and preserve more details of the non-focus regions, which is superior to conventional methods in visual inspection and objective evaluations. [ABSTRACT FROM AUTHOR]

This research proposes an improved hybrid fusion scheme for non‐subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub‐bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub‐bands. The entropy of square of the coefficients and weighted sum‐modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image. [ABSTRACT FROM AUTHOR]

This article proposes a novel feature extraction approach for speech/music classification based on generalized Gaussian distribution descriptors extracted from IIR-CQT spectrogram representation. IIR-CQT spectrogram visual representation provides superior temporal resolution at high frequencies and better spectral resolution for low frequencies compared to the conventional short-time Fourier transform analysis which provides uniform frequency resolution. Multi-level decomposition of the spectrogram image is then performed using the Nonsubsampled Contourlet Transform (NSCT) which a fully shift-invariant, multi-scale, and multi-direction expansion that can preserve the edges of the textural pattern of speech and music. The generalized Gaussian distribution (GGD) parameters are produced using maximum likelihood estimation (MLE) from the NSCT subbands to create the image feature descriptor. Chaos crow search algorithm is employed to chose the most relevant feature sub-set and to discard redundant features and finally the extreme learning machine classifier categorizes input audio segment into speech/music. The experimental results show that the proposed feature descriptor is effective and performs better compared to the existing approaches in the speech/music classification. In addition, mismatched training and testing results are also presented. [ABSTRACT FROM AUTHOR]

This article presents an effective method for image denoising using non-local means with packing of multi patches (NLM-PMP) in non-subsampled contourlet (NSC) domain which works based on multi-scale decomposition and directionality. Guided filter with image statistics (SGIF) is utilized to further process the obtained denoised image for mitigation of ringing artifacts those are invariably appeared, which leads to the local structure preservation like textures, edges and small details efficiently. The authors decomposed the image into low-pass subband coefficients and bandpass subband coefficients including the base and the detail scales. The low frequency noise in the base sub-band and the edges with small textural details in the detail scale are processed independently using an NLM-PMP filter. In addition, SGIF is applied to enhance the visual perception of denoised image. Experimental results indicate that the proposed approach is competitive at lower noise strength with respect to peak signal to noise ratio and structural similarity index measure and excels in performance at higher noise strength compared with several state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

In order to reduce the Gaussian noise introduced during image generation, this paper presents an image denoising algorithm based on variational Bayes (V-Bayes) estimation and nonsubsampled contourlet transform (NSCT) bivariate model. First, the proposed algorithm uses the NSCT's advantages of translation-invariant and multidirection-selectivity, exploits the intra-scale and inter-scale correlations of NSCT coefficients. Then, the corresponding nonlinear bivariate threshold function of the model is deduced by using V-Bayes estimation theory. Finally, the noise-reduced coefficients are inverse-transformed by NSCT to obtain denoised image. The simulation results show that the denoised image has obvious improvement in subjective visual effects and performance indicators, and effectively preserves the details and texture information in the original image. [ABSTRACT FROM AUTHOR]

Streaks due to weather, such as rain or snow, degrade image quality and affect the performance of subsequential high-level vision tasks by the generated undesired artifacts. Hence, removing streak interference is an ongoing and challenging issue for many applications in realtime mobile surveillance systems. In this paper, streak interference removal from a single image is the focus. To sufficiently extract streak interference from an observed image, the image was firstly filtered with the nonsubsampled contourlet transform. Then, the residual part between the original and filtered image was decomposed into the streak component and detail component of background. Based on the additive layer model, we designed two specific priors that constrain the detail and streak interference respectively and established a model with joint priors for residual image decomposition. As a result, the resulting image can be synthesized with the filtered image and detail component. Experimental results show that our proposed method outperforms existing methods both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

In this paper, researching the correlation property of congenetic subband coefficients of Nonsubsampled Contourlet Transform (NSCT), and proposing a new adaptive denoising theory. This theory combines the excellent shift-invariant of NSCT with the correlation property of congenetic subband coefficients in image denoising. The correlation property contains the entire relational correlation value of the congenetic subband coefficients, and the grey correlation value in single coefficient of the congenetic subband coefficients of the NSCT. According to the correlation property of congenetic subband coefficients, the new algorithm can automatically identify the strong edges, weak edges and noise in the noisy image, and then it can filter the noise and preserve the strong edges and weak edges at certain degree. The experiment results are provided to compare with the elegant non-local means method, which show that the theory proposed in this paper has good effect. [ABSTRACT FROM AUTHOR]

Marine raft aquaculture (MFA) plays an important role in the marine economy and ecosystem. With the characteristics of covering a large area and being sparsely distributed in sea area, MFA monitoring suffers from the low efficiency of field survey and poor data of optical satellite imagery. Synthetic aperture radar (SAR) satellite imagery is currently considered to be an effective data source, while the state-of-the-art methods require manual parameter tuning under the guidance of professional experience. To preclude the limitation, this paper proposes a segmentation network combined with nonsubsampled contourlet transform (NSCT) to extract MFA areas using Sentinel-1 images. The proposed method is highlighted by several improvements based on the feature analysis of MFA. First, the NSCT was applied to enhance the contour and orientation features. Second, multiscale and asymmetric convolutions were introduced to fit the multisize and strip-like features more effectively. Third, both channel and spatial attention modules were adopted in the network architecture to overcome the problems of boundary fuzziness and area incompleteness. Experiments showed that the method can effectively extract marine raft culture areas. Although further research is needed to overcome the problem of interference caused by excessive waves, this paper provides a promising approach for periodical monitoring MFA in a large area with high efficiency and acceptable accuracy.

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.