Non-subsampled Shearlet Transform Research Articles

A Hybrid Approach for CT Image Noise Reduction Combining Method Noise-CNN and Shearlet Transform

The presence of gaussian noise commonly weakens the diagnostic precision of low-dose CT imaging. A novel CT image denoising technique that integrates the non-subsampled shearlet transform (NSST) with Bayesian thresholding, and incorporates a modern method noise Deep Convolutional neural network (DCNN) based post-processing operation on denoised images to strengthen low-dose CT imaging quality. The hybrid method commences with NSST and Bayesian thresholding to mitigate the initial noise while preserving crucial image features, such as corners and edges. The novel aspect of the proposed approach is its successive application of a DnCNN on initial denoised image, which learns and removes residual noise patterns from denoised images, thereby enhancing fine detail preservation. This dual-phase strategy addresses both noise suppression and image-detail preservation. The proposed technique is evaluated through the use of metrics, such as PSNR, SNR, SSIM, ED, and UIQI. The results demonstrate that the hybrid approach outperforms standard denoising techniques in preserving image quality and fine details.

Research on fractional wavelet transform combined with parameter adaptive PCNN for infrared and visible image fusion algorithm

Infrared and visible image fusion holds significant value across various fields due to its ability to provide complementary information. However, existing fusion algorithms often suffer from susceptibility to external physical conditions of source images, leading to inconsistent fusion quality and limited adaptive capability. In this paper, we propose a fractional wavelet transform fusion algorithm for infrared and visible images. This algorithm uses fractional wavelet domain image decomposition to more accurately locate the image structures of different scales. Initially, we perform image decomposition using a non-subsampled shearlet transform (NSST) with multi-scale and multi-directional decomposition. Subsequently, to effectively fuse detailed information, we employ discrete fractional wavelet transform (DFRWT) to decompose low-frequency subbands while selecting an appropriate p-value. High-frequency subbands are fused using a parameterized adaptive pulse-coupled neural network (PCNN) model. To validate the superiority of our algorithm, we conduct visual and quantitative comparative analyses against several existing algorithms. The results confirm that our proposed algorithm exhibits robustness against external physical conditions and surpasses these existing algorithms, making it highly applicable for infrared–visible fusion tasks.

Detection and Segmentation of Glioma Tumors Utilizing a UNet Convolutional Neural Network Approach with Non-Subsampled Shearlet Transform.

The prompt and precise identification and delineation of tumor regions within glioma brain images are critical for mitigating the risks associated with this life-threatening ailment. In this study, we employ the UNet convolutional neural network (CNN) architecture for glioma tumor detection. Our proposed methodology comprises a transformation module, a feature extraction module, and a tumor segmentation module. The spatial domain representation of brain magnetic resonance imaging images undergoes decomposition into low- and high-frequency subbands via a non-subsampled shearlet transform. Leveraging the selective and directive characteristics of this transform enhances the classification efficacy of our proposed system. Shearlet features are extracted from both low- and high-frequency subbands and subsequently classified using the UNet-CNN architecture to identify tumor regions within glioma brain images. We validate our proposed glioma tumor detection methodology using publicly available datasets, namely Brain Tumor Segmentation (BRATS) 2019 and The Cancer Genome Atlas (TCGA). The mean classification rates achieved by our system are 99.1% for the BRATS 2019 dataset and 97.8% for the TCGA dataset. Furthermore, our system demonstrates notable performance metrics on the BRATS 2019 dataset, including 98.2% sensitivity, 98.7% specificity, 98.9% accuracy, 98.7% intersection over union, and 98.5% disc similarity coefficient. Similarly, on the TCGA dataset, our system achieves 97.7% sensitivity, 98.2% specificity, 98.7% accuracy, 98.6% intersection over union, and 98.4% disc similarity coefficient. Comparative analysis against state-of-the-art methods underscores the efficacy of our proposed glioma brain tumor detection approach.

Dual image watermarking based on NSST-LWT-DCT for color image

Advanced internet technology allows unauthorized individuals to modify and distribute digital images. Image watermarking is a popular solution for copyright protection and ensuring digital security. This research presents an embedding scheme with a set of conditions using non-subsampled Shearlet transform (NSST), lifting wavelet transform (LWT), and discrete cosine transform (DCT). Red and green channels are employed for the embedding process. The red channel is converted by NSST-LWT. The low-frequency area (LL) frequency is then split into small blocks of 8×8, each partition block is then transformed by DCT. The DCT coefficient of (3,4), (5,2), (5,3), (3,5), called matrix M1, and (2,5), (4,3), (6,2), (4,4), called matrix M2 are selected for singular value decomposition (SVD) process. With a set of conditions, the watermark bits are incorporated into those singular values. The green channel is cropped to get the center image before splitting into 4×4 pixels. The block components are then selected based on the least entropy value for the embedding regions. The selected blocks are then computed using LWT-SVD. A set of conditions for U(1,1) and U(2,1) are used to incorporate the watermark logo. The experimental findings reveal that the suggested scheme achieves high imperceptibility and resilience under various evaluating attacks with an average peak signal-to-noise ratio (PSNR) and correlation value (NC) values are up to 43.89 dB and 0.96, respectively.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

Enhancement of X-Ray Images by Gradient Domain Guided Filtering and Non Sub Sampled Shearlet Transform

Abstract: In order to address the issues of low resolution, noise amplification, missing details, and weak edge gradient retention in the X-ray image improvement process, we propose in this study an image enhancement technique that combines nonSubsampled Shearlet transform with gradient-domain guided filtering. We start by breaking down the non Subsampled Shearlet transform and histogram equalization into the original image. We receive multiple high-frequency sub-bands as well as a lowfrequency sub-band. To enhance the overall contrast of the image and bring out the contour information in the low-frequency sub-band, adaptive gamma correction with weighting distribution is applied. To reduce image noise and emphasize edge information and detail, gradient-domain guided filtering is applied to the high-frequency sub-bands. In order to generate the final enhanced image, we use the inverse non-Subsampled Shearlet transform to reconstruct all of the sub-bands that were successfully processed. The experimental findings demonstrate the effectiveness of the suggested algorithm in improving X-ray images, as well as the clear advantages its objective index provides over certain conventional algorithms.

Multi-modal medical image fusion using improved dual-channel PCNN.

This paper proposes a medical image fusion method in the non-subsampled shearlet transform (NSST) domain to combine a gray-scale image with the respective pseudo-color image obtained through different imaging modalities. The proposed method applies a novel improved dual-channel pulse-coupled neural network (IDPCNN) model to fuse the high-pass sub-images, whereas the Prewitt operator is combined with maximum regional energy (MRE) to construct the fused low-pass sub-image. First, the gray-scale image and luminance of the pseudo-color image are decomposed using NSST to find the respective sub-images. Second, the low-pass sub-images are fused by the Prewitt operator and MRE-based rule. Third, the proposed IDPCNN is utilized to get the fused high-pass sub-images from the respective high-pass sub-images. Fourth, the luminance of the fused image is obtained by applying inverse NSST on the fused sub-images, which is combined with the chrominance components of the pseudo-color image to construct the fused image. A total of 28 diverse medical image pairs, 11 existing methods, and nine objective metrics are used in the experiment. Qualitative and quantitative fusion results show that the proposed method is competitive with and even outpaces some of the existing medical fusion approaches. It is also shown that the proposed method efficiently combines two gray-scale images.

Novel fusion strategy for image fusion using rescue hunt optimization-based modified guidance model

ABSTRACT A new method for image fusion introduces an inventive strategy for amalgamating data from multiple images, resulting in the creation of a single, improved output image. This approach aims to address the limitations and challenges associated with conventional fusion techniques, paving the way for improved results in various applications. In this research, a novel approach for image fusion is presented, featuring a rescue hunt optimisation-based modified guidance model (RHO-based MG model). The methodology leverages the non-subsampled contourlet transform (NSCT) and non-subsampled shear let transform (NSST) to construct the fusion transform using two input images, typically infrared and visual images. By hybridising high-frequency (HF) and low-frequency (LF) bands from both types of images, the fusion model generates the final HF and LF bands. A distinctive modified guidance strategy is employed in the development of these bands. The proposed approach utilises a rescue hunt algorithm developed through the combination of search and rescue optimisation (SARO) and grey wolf optimisation (GWO) behaviours. In image fusion, optimisation fine-tunes VGG-19 and RESNET 18 models to improve their ability to combine multiple images effectively. This process involves adjusting the models’ internal parameters using optimisation techniques. By analysing the features in different input images, these models learn to extract meaningful information and create a fused image that retains the important details from each source. This strategy is further enhanced by integrating the VGG-19 and RESNET 18 models. The fused image is composed of combined HF and LF bands, with the final result obtained through an inverse hybrid transform. Experimental results, conducted on a dataset of 25 images, demonstrate the effectiveness of the approach with metrics average gradient (AG), edge intensity (EI), PSNR, RMSE, SSIM, and variance attained the values of 24.71, 236.67, 54.96 dB, 0.22, 63.16, and 0.11, respectively. This innovative method offers a promising direction for enhancing image fusion quality and is highlighted by its unique integration of optimisation and guidance strategies.

Self-attention-based generative adversarial network optimized with color harmony algorithm for brain tumor classification

ABSTRACT This paper proposes a novel approach, BTC-SAGAN-CHA-MRI, for the classification of brain tumors using a SAGAN optimized with a Color Harmony Algorithm. Brain cancer, with its high fatality rate worldwide, especially in the case of brain tumors, necessitates more accurate and efficient classification methods. While existing deep learning approaches for brain tumor classification have been suggested, they often lack precision and require substantial computational time.The proposed method begins by gathering input brain MR images from the BRATS dataset, followed by a pre-processing step using a Mean Curvature Flow-based approach to eliminate noise. The pre-processed images then undergo the Improved Non-Sub sampled Shearlet Transform (INSST) for extracting radiomic features. These features are fed into the SAGAN, which is optimized with a Color Harmony Algorithm to categorize the brain images into different tumor types, including Gliomas, Meningioma, and Pituitary tumors. This innovative approach shows promise in enhancing the precision and efficiency of brain tumor classification, holding potential for improved diagnostic outcomes in the field of medical imaging. The accuracy acquired for the brain tumor identification from the proposed method is 99.29%. The proposed BTC-SAGAN-CHA-MRI technique achieves 18.29%, 14.09% and 7.34% higher accuracy and 67.92%,54.04%, and 59.08% less Computation Time when analyzed to the existing models, like Brain tumor diagnosis utilizing deep learning convolutional neural network with transfer learning approach (BTC-KNN-SVM-MRI); M3BTCNet: multi model brain tumor categorization under metaheuristic deep neural network features optimization (BTC-CNN-DEMFOA-MRI), and efficient method depending upon hierarchical deep learning neural network classifier for brain tumour categorization (BTC-Hie DNN-MRI) respectively.

A Novel Hybrid Multimodal Medical Image Fusion Scheme Based on Non-subsampled Shearlet Transform

A Novel Hybrid Multimodal Medical Image Fusion Scheme Based on Non-subsampled Shearlet Transform

Infrared and Visible Image Fusion Based on Saliency Detection Dynamic Threshold Neural P Systems in Non-Subsampled Shearlet Transform Domain

Infrared and Visible Image Fusion Based on Saliency Detection Dynamic Threshold Neural P Systems in Non-Subsampled Shearlet Transform Domain

Adaptive Non-subsampled Shearlet Transform and its Application to Surface Wave Suppression

Adaptive Non-subsampled Shearlet Transform and its Application to Surface Wave Suppression

Blind image watermark decoder in NSST-FPCET domain using Weibull Mixtures-HMT

Balancing imperceptibility, robustness, and data payload is a key topic in digital watermarking technology. Many contributions point to statistical modeling as an effective solution to this problem. On the basis of this, in this paper, we devise a hybrid domain image watermark decoder based on Weibull mixtures based hidden Markov tree (Weibull Mixtures-HMT) model. In the embedding phase, Otsu-Canny edge detection method is used to map the positions of high entropy blocks to the target subband obtained by non-subsampled shearlet transform (NSST), and fast polar complex exponential transform (FPCET) is computed in the target blocks to obtain the NSST-FPCET domain. The watermark signals are embedded into NSST-FPCET magnitudes by a linear method. In the extraction phase, NSST-FPCET magnitude coefficients are modeled by Weibull Mixtures-HMT model, which describes the distribution characteristics as well as the dependencies of NSST-FPCET magnitudes. The efficient variance reduced stochastic expectation maximization method is employed for estimating the parameters of Weibull Mixtures-HMT model. A statistical image watermark decoder in NSST-FPCET domain is finally achieved by the maximum likelihood decision. Massive experiments are performed so as to confirm the superiority of the designed scheme in trade-off among the data payload, imperceptibility, and robustness.

A dual fusion deep convolutional network for blind universal image denoising

Blind image denoising and edge-preserving are two primary challenges to recover an image from low-level vision to high-level vision. Blind denoising requires a single denoiser can denoise images with any intensity of noise, and it has practical utility since accurate noise levels cannot be acquired from realistic images. On the other hand, edge preservation can provide more image features for subsequent processing which is also important for the denoising. In this paper, we propose a novel blind universal image denoiser to remove synthesis and realistic noise while preserving the image texture. The denoiser consists of noise network and prior network parallelly, and then a fusion block is used to give the weight between these two networks to balance computation cost and denoising performance. We also use the Non-subsampled Shearlet Transform (NSST) to enlarge the size of receptive field to obtain more detailed information. Extensive denoising experiments on synthetic images and realistic images show the effectiveness of our denoiser.

A novel framework of multimodal medical image fusion using adaptive NSST and optimized deep learning approach

ABSTRACT Multimodal medical image fusion plays a pivotal role in the medical and imaging industry. Existing works of deep learning method suffers from blurred texture characteristics and computing efficiency. Thus, a novel deep learning model is proposed for multimodal medical image fusion. Initially, an Adaptive Non-Subsampled Shearlet Transform (ANSST) approach is developed for decomposing the images, where the filter coefficient is optimized by Hybrid Water Strider-Dingo Optimization (HWS-DOX). The fusion of the high sub-bands of source image 1 and high sub-bands of source image 2 is done by an Optimized Deep Neural Network (ODNN). Then, the lower sub-bands of source image 1 and lower sub-bands of source image 2 will be fused through a weighted averaging scheme. Finally, the final fused images are attained by applying the inverse ANSST. Thus, the performance of recommended module is compared over classical heuristics and various transform methods.

TDFusion: When Tensor Decomposition Meets Medical Image Fusion in the Nonsubsampled Shearlet Transform Domain.

In this paper, a unified optimization model for medical image fusion based on tensor decomposition and the non-subsampled shearlet transform (NSST) is proposed. The model is based on the NSST method and the tensor decomposition method to fuse the high-frequency (HF) and low-frequency (LF) parts of two source images to obtain a mixed-frequency fused image. In general, we integrate low-frequency and high-frequency information from the perspective of tensor decomposition (TD) fusion. Due to the structural differences between the high-frequency and low-frequency representations, potential information loss may occur in the fused images. To address this issue, we introduce a joint static and dynamic guidance (JSDG) technique to complement the HF/LF information. To improve the result of the fused images, we combine the alternating direction method of multipliers (ADMM) algorithm with the gradient descent method for parameter optimization. Finally, the fused images are reconstructed by applying the inverse NSST to the fused high-frequency and low-frequency bands. Extensive experiments confirm the superiority of our proposed TDFusion over other comparison methods.

A pansharpening method combining iterative filtering and NSST-NSML-PAPCNN to optimize spatial detail extraction and injection

ABSTRACT Spatial injection-based pansharpening methods are prone to spatial or spectral distortions in pansharpening images due to insufficient extraction of spatial details and a mismatch between the amount of spatial detail information injected and the required amount. To this end, this paper proposes a pansharpening method that optimizes spatial detail extraction and injection. Firstly, a method to optimize the amount of spatial detail injection is proposed, that is, to extract the high-frequency information of the image through iterative filtering and determine the optimal number of iterations based on the global analysis of the method. Then, to fully extract and combine the spatial detail information of the source image, the detailed high-frequency image extracted corresponding to the optimal iterative filtering times is decomposed by non-subsampled shearlet transform (NSST), and a new multi-scale sum-modified-Laplacian (NSML) as an external stimulus to a parameter-adaptive pulse-coupled neural network model (PAPCNN). A fusion rule based on multi-scale morphological gradients is designed to extract a small amount of detailed information for the low-frequency subband. The fused spatial detail image can be obtained by combining the fused low-frequency and high-frequency subbands and inverse NSST transformation. Finally, pansharpening can be realized by combining spatial detail image, injection coefficient, and MS image. In this paper, many experiments are carried out on the QuickBird, GeoEye-1, and WorldView-4 datasets, and quantitative and qualitative comparisons are made with eight advanced methods. Experimental results show that the method proposed in this paper can achieve better fusion results.

Multimodal Fusion-Based Image Hiding Algorithm for Secure Healthcare System

The development of artificial intelligence (AI) plays a significant role of multimedia applications, especially in the healthcare domain. However, it has brought about the problem of sensitive information leakage. To address these challenges, an interesting multimodal fusion-based robust image hiding algorithm is proposed in this paper. Firstly, fused image is considered as mark image generated from MRI and CT images using non-subsampled shearlet transform (NSST). Secondly, we employed principal component analysis (PCA) to compute the appropriate coefficients of cover image for embedding purpose. Thirdly, fused mark image is Arnold cat map encoded to address the security issue hidden mark media. Finally, the fusion of fractional dual-tree complex wavelet transform (Fr-DTCWT) and randomized singular value decomposition (RSVD) is utilized to conceal encrypted fused mark media inside host image. Our findings show that the proposed algorithm outperforms some of the recent techniques in terms of high robustness and invisibility.

Multimodal medical image fusion in NSST domain with structural and spectral features enhancement

For the past 25 years, medical imaging has been extensively used for clinical diagnosis. The main difficulties in medicine are accurate disease recognition and improved therapy. Using a single imaging modality to diagnose disease is challenging for clinical personnel. In this paper, a novel structural and spectral feature enhancement method in NSST Domain for multimodal medical image fusion (MMIF) is proposed. Initially, the proposed method uses the Intensity, Hue, Saturation (IHS) method to generate two pairs of images. The input images are then decomposed using the Non-Subsampled Shearlet Transform (NSST) method to obtain low frequency and high frequency sub-bands. Next, a proposed Structural Information (SI) fusion strategy is employed to Low Frequency Sub-bands (LFS's). It will enhance the structural (texture, background) information. Then, Principal Component Analysis (PCA) is employed as a fusion rule to High Frequency Sub-bands (HFS's) to obtain the pixel level information. Finally, the fused final image is obtained by employing inverse NSST and IHS. The proposed algorithm was validated using different modalities containing 120 image pairs. The qualitative and quantitative results demonstrated that the algorithm proposed in this research work outperformed numerous state-of-the-art MMIF approaches.

Gray wolf optimization and image enhancement with NLM Algorithm for multimodal medical fusion imaging system

Multimodal Medical fusion imaging is a significant aspect of image-guided medical diagnosis. A new improved non-sub sampled shearlet transform (NSST) based Multimodal Medical Fusion imaging System based on Gray Wolf Optimization (GWO) and Image Enhancement with NLM Algorithmic computer guided medical procedures. This paper proposes an efficient medical fusion imaging system that is evolved from the concept of Non-Sub sampled Shearlet Transform and the Gray Wolf Optimization algorithm. The initial procedure in this system includes histogram matching among one image and the other to facilitate common dynamic range for both images. Decomposition of multimodal medical images is done by NSST to be fused into their coefficients. Optimum decomposition level is scaled by EGOA with optimal gain parameters. Furthermore, denoising with enhancement procedure is performed to enhance its visual quality. If the images are corrupted by noise, the these traditional fusion methods performance is greatly hampered. Therefore, this is essential to create a fusion approach that can maintain accurate information even while distorting the images.