Image Smoothing Research Articles

An anti-aliasing filtering of quantum images in the spatial domain using a pyramid structure

Abstract As a part of quantum image processing, quantum image filtering is a crucial technology in the development of quantum computing. Low-pass filtering can effectively achieve anti-aliasing effects on images. Currently, most quantum image filtering is based on classical domains and grayscale images, with relatively fewer studies on anti-aliasing in the quantum domain. This paper proposes a scheme for anti-aliasing filtering based on quantum grayscale and color image scaling in the spatial domain. It achieves the effect of antialiasing filtering on quantum images during the scaling process. First, we use the novel Enhanced Quantum Representation (NEQR) and the Improved Quantum Representation of Color Images (INCQI) to represent classical images. Since aliasing phenomena are more pronounced when images are scaled down, this paper focuses only on the anti-aliasing effects in the case of reduction. Subsequently, we perform anti-aliasing filtering on the quantum representation of the original image and then use bilinear interpolation to scale down the image, achieving the anti-aliasing effect. The constructed pyramid model is then used to select an appropriate image for upscaling to the original image size. Finally, the complexity of the circuit is analyzed.Compared to the images experiencing aliasing effects solely due to scaling, applying anti-aliasing filtering to the images results in smoother and clearer outputs. Additionally, it allows for manual intervention to select the desired level of image smoothness.

Fingerprint based authentication framework for IoT environment

<p>Internet of Things (IoT) approach makes the humans life easy and safe and due to its acceptability and portability. Humans are surrounded by multitude of heterogeneous devices which assist them in their daily routine. The integration of Blockchain technology with IoT played an important role in medical science and online transaction. At same point of time, the IoT network and devices are vulnerable that can be exploit to affect the network and devices security and accessibility. Therefore, robust authentication techniques should be implementing to secure the IoT network as well as devices accessibility. Due to high heterogeneity of applications, providing authentication for these devices is always challengeable. In this work, a novel fingerprint-based authentication framework in implemented in which Gaussian filter is used for image smoothness, Robert filter for edge detection and achieve 94% accuracy while authentication.</p>

Weighted sparse gradient reconstruction model with a robust fidelity for edge-aware image smoothing

Weighted sparse gradient reconstruction model with a robust fidelity for edge-aware image smoothing

SuperPCA and 2-D compact variational mode decomposition for feature extraction from hyperspectral images

ABSTRACT The 2-D compact variational mode decomposition (2-D-C-VMD) has potential research significance in data mining of hyperspectral images (HSIs). The paper proposes a method of concatenation of features. First, the 2-D-C-VMD technique is used to decompose the HSIs to obtain smooth and clear boundary sub-band images when the 2-D-C-VMD takes the most preferred a priori parameter K × . Second, the optimal low-frequency component features of the image and the optimal high-frequency component constructed features are concatenated first and last, and the structure of the concatenation is constructed into a new fused data. Finally, in order to avoid the redundancy of the fused data band information from affecting the classification result, combine a recently developed simple and practical superpixelwise principal component analysis (SuperPCA) dimensionality reduction method to effectively reduce the dimensionality of the fused data. By comparing the analysis with relying only on the optimal low-frequency image feature information and with the direct linear superposition fusion that has been employed, it is verified that the concatenation of features approach proposed in this paper can obtain better recognition results.

Real-time ghost imaging algorithm on the multidimensional vector matrix Walsh transformation with spatiotemporal free-fps.

In this research, we propose a real-time spatiotemporal free-fps algorithm based on the multidimensional vector matrix Walsh transform with an adjustable ghost imaging video quality and a frame rate to address the issues of uneven imaging speed, fixed imaging frame rate, and uncomfortable appearance in real-time ghost imaging videos of moving objects. This algorithm utilizes the temporal and spatial correlation of ghost imaging videos to achieve free and adjustable video frame rates in time without being limited by DMD refresh rates. Improving the spatial information of a single frame in space enhances the smoothness of ghost imaging videos, making the appearance of ghost imaging videos more comfortable. To achieve this, a four-dimensional vector Walsh transform kernel matrix is used to transform and reconstruct the high-quality images of the target object. Then the reconstructed high-quality image is spatially interpolated to enhance spatial information. Reasonable frame rate parameters are set based on the corresponding relationship between the detection values of the adjacent frames and speckle, improving the ghost imaging video in both time and space and achieving a smooth real-time ghost imaging video with an adjustable quality and frame rate. The simulation and experimental results of moving objects show that our algorithm solves the limitation of a DMD refresh rate compared with the existing ghost imaging video methods and makes the ghost imaging video more comfortable and smoother in real time. The PSNR of the objective evaluation index is increased by 12%. Regarding a subjective evaluation, this paper proposes an adaptive parameterless evaluation algorithm (APEA) for images with different resolutions based on the NRSS, which improved the structure retention degree by 13% and the Brisque parameter evaluation by 70%. We propose an adaptive parameterless video comfort evaluation algorithm (APVCEA) to evaluate the subjective comfort of ghost imaging videos by 14% compared with traditional methods.

Using image smoothing structure information to guide image inpainting

Using image smoothing structure information to guide image inpainting

Cubic Polishing Spline-Based Algorithms for Industrial Image Processing

Industrial image processing is a major technology in the fourth industrial revolution. In recent years, the spline method has gained more and more attention in image processing. To explore an efficient reconstruction algorithm, this paper proposes novel cubic polishing spline image processing algorithms. The cubic polishing spline is a high precision spline algorithm that brings more degrees of freedom by adding nodes to better balance the local and global aspects. First, the direct and indirect cubic polishing spline transforms are used for the image interpolation filter. Then, we construct the least squares cubic polishing spline and pyramid approximation method with application to the image reconstruction and image scaling (image upscaling or downscaling). Next, the frequency domain cubic polishing spline lowpass filter and highpass filter are proposed, and the proposed schemes are applied to image smoothing and image sharpening respectively. Finally, the PSNR and SSIM and RMSE evaluation metrics are used to illustrate the reconstruction effects, and experiments show that the algorithm proposed in this paper can achieve better reconstruction results with better fidelity and competitiveness compared with other schemes.

Automated monitoring of high-voltage transmission lines based on machine vision

Abstract This paper takes the automated monitoring of high-voltage transmission lines as the entry point and constructs an automated monitoring model of high-voltage transmission lines based on machine vision. Through the transmission equipment recognition algorithm to identify the type and location of the equipment in the inspection image, using image processing methods for the collected high-voltage transmission line image color image grayscaling and grayscale image stretching, and then grayscale image smoothing and segmentation, so as to achieve the identification of the location of high-voltage transmission line fault location. Simulation test for image processing and transmission equipment identification methods, and then carry out automated monitoring experiments with high-voltage transmission line conductor stranding as an example to verify the feasibility and reliability of the model in this paper. The results show that for the automatic detection of individual conductor strand breakage, the method of this paper detects the number of strand breakage is only 1-2 less or more than the actual number of strand breakage; there is a certain difference, but basically in line with the actual situation, and the method of this paper, on average, detects the efficiency of the method is faster than the traditional method by 5.69~7.18 s/m. The research to enhance the quality of automated monitoring of high-voltage power transmission lines has a certain application value.

Magnetic resonance and computer tomography image fusion using novel weight maps obtained by using median and guided filters

This paper proposes a technique for medical picture fusion based on the guided image filter. It utilizes guided filtering to smooth images, using texture information as guidance for the filter. Weight maps of the detail images are created through pixel weight computation based on image statistics. Finally, the source images are combined using a weighted average combining approach. The effectiveness of the proposed method is evaluated in terms of multiple quantitative image quality assessment factors and compared with several state-of-the-art image fusion algorithms. Experimental results indicate that the suggested approach for image fusion is effective.

A New Fusion Feature Selection Model (FFSM) based Feature Extraction System for Hand Gesture Recognition

The research presented here proposes a unique approach for exact feature extraction that makes use of the Fusion Feature Selection Model (FFSM). The method encompasses various stages to preprocess and extract crucial features from first-person hand action (FHPA) images. Preprocessing involves video-to-frame conversion, RGB to grayscale conversion, an improved median filter, and Gaussian blur-based image smoothing. Segmentation is achieved using the Improved SwinNet to identify meaningful regions within the images. Feature extraction employs the Gabor Line Derivative (GLD) method, Active Shape Model (ASM), and Histogram of Oriented Gradients (HOG) to capture texture, edge, and shape information, respectively. Extensive experimental evaluations demonstrate the effectiveness of our proposed approach, achieving remarkable performance in accurate feature extraction tasks.

A predictor-corrector phase unwrapping algorithm for temporally undersampled gradient-echo MRI.

To develop a method for unwrapping temporally undersampled and nonlinear gradient recalled echo (GRE) phase. Temporal unwrapping is performed as a sequential one step prediction of the echo phase, followed by a correction to the nearest integer wrap-count. A spatio-temporal extension of the 1D predictor corrector unwrapping (PCU) algorithm improves the prediction accuracy, and thereby maintains spatial continuity. The proposed method is evaluated using numerical phantom, physical phantom, and in vivo brain data at both 3 T and 9.4 T. The unwrapping performance is compared with the state-of-the-art temporal and spatial unwrapping algorithms, and the spatio-temporal iterative virtual-echo based Nyquist sampled (iVENyS) algorithm. Simulation results showed significant reduction in unwrapping errors at higher echoes compared with the state-of-the-art algorithms. Similar to the iVENyS algorithm, the PCU algorithm was able to generate spatially smooth phase images for in vivo data acquired at 3 T and 9.4 T, bypassing the use of additional spatial unwrapping step. A key advantage over iVENyS algorithm is the superior performance of PCU algorithm at higher echoes. PCU algorithm serves as a robust phase unwrapping method for temporally undersampled and nonlinear GRE phase, particularly in the presence of high field gradients.

Flexible and accurate total variation and cascaded denoisers-based image reconstruction algorithm for hyperspectrally compressed ultrafast photography

Hyperspectrally compressed ultrafast photography (HCUP) based on compressed sensing and time- and spectrum-to-space mappings can simultaneously realize the temporal and spectral imaging of non-repeatable or difficult-to-repeat transient events with a passive manner in single exposure. HCUP possesses an incredibly high frame rate of tens of trillions of frames per second and a sequence depth of several hundred, and therefore plays a revolutionary role in single-shot ultrafast optical imaging. However, due to ultra-high data compression ratios induced by the extremely large sequence depth, as well as limited fidelities of traditional algorithms over the image reconstruction process, HCUP suffers from a poor image reconstruction quality and fails to capture fine structures in complex transient scenes. To overcome these restrictions, we report a flexible image reconstruction algorithm based on a total variation (TV) and cascaded denoisers (CD) for HCUP, named the TV-CD algorithm. The TV-CD algorithm applies the TV denoising model cascaded with several advanced deep learning-based denoising models in the iterative plug-and-play alternating direction method of multipliers framework, which not only preserves the image smoothness with TV, but also obtains more priori with CD. Therefore, it solves the common sparsity representation problem in local similarity and motion compensation. Both the simulation and experimental results show that the proposed TV-CD algorithm can effectively improve the image reconstruction accuracy and quality of HCUP, and may further promote the practical applications of HCUP in capturing high-dimensional complex physical, chemical and biological ultrafast dynamic scenes.

Programmable comprehensive controller for multi-color 3D confocal spinning-disk image scanning microscope

This paper introduces a compact, portable, and highly accurate triggering control system for a 3D confocal spinning-disk image scanning microscope (CSD-ISM). Building upon on our previously published research, we expanded the hardware of the controller and synchronized it with a sub-micron translator which scans the object in the z-direction. As well as expanding the hardware, the software also was extended from previously published work similarly as it is stated for hardware while allowing full control over the 3D movement. We showed a clear and smooth 3D image made up of a collection of 2D images at different heights.

Image smoothing combining edge-consistency with region-piecewise flatting

Image smoothing constitutes a fundamental task within the realm of computer vision. Effectively preserving the structural integrity of edges during the image smoothing process represents an arduous challenge. Current methodologies persistently encounter issues in adequately exploiting and harnessing the intrinsic value of edge information within images, while simultaneously suffering from the limitation of employing overly simplistic constraints for achieving desirable smoothing outcomes. To solve these problems, we propose a new Edge Consistency and Region Piecewise Flatting Network (ECRPF-Net). To circumvent the impact of the convolution process on edge-preserving properties, our network adopts the approach of superimposing significant edge information onto the feature layer, while also incorporating a weak structure reinforcement module. To efficiently preserve the edge structure, we incorporate an edge consistency module (ECM), which utilizes the edge-response consistency relationship between the input and output images to ensure the preservation of edges. To enhance the quality of image smoothing and mitigate the occurrence of edge artifacts, we introduce the Region Piecewise Flatting Module (RPFM). This module partitions the image into different regions based on the similarities and differences in edge response features, and applies a flexible sub-region smoothing approach to constrain the final output, ensuring superior smoothing results. Experimental results demonstrate the excellent performance of the ECRPF-Net in preserving the prominent edge structure of the image, achieving more visual-appealing smoothing outcomes, and surpassing the majority of existing methods on public datasets.

A New Fractional-order Derivative-based Nonlinear Anisotropic Diffusion Model for Biomedical Imaging

Medical imaging, the process of visual representation of different organs and tissues of the human body, is employed for monitoring the normal as well as abnormal anatomy and physiology of the body. Imaging which can provide healthcare solutions ensuring a regular measurement of various complex diseases plays a critical role in the diagnosis and management of many complex diseases and medical conditions, and the quality of a medical image, which is not a single factor but a composite of contrast, artifacts, distortion, noise, blur, and so forth, depends on several factors such as the characteristics of the equipment, the imaging method in question as well as the imaging variables chosen by the operator. The medical images (ultrasound image, X-rays, CT scans, MRIs, etc.) may lose significant features and become degraded due to the emergence of noise as a result of which the process of improvement pertaining to medical images has become a thought-provoking area of inquiry with challenges related to detecting the speckle noise in the images and finding the applicable solution in a timely manner. The partial differential equations (PDEs), in this sense, can be used extensively in different aspects with regard to image processing ranging from filtering to restoration, segmentation to edge enhancement and detection, denoising in particular, among the other ones. In this research paper, we present a conformable fractional derivative-based anisotropic diffusion model for removing speckle noise in ultrasound images. The proposed model providing to be efficient in reducing noise by preserving the essential image features like edges, corners and other sharp structures for ultrasound images in comparison to the classical anisotropic diffusion model. Furthermore, we aim at proving the viscosity solution of the fractional diffusion model. The finite difference method is used to discretize the fractional diffusion model and classical diffusion models. The peak signal-to-noise ratio (PSNR) is used for the quality of the smooth images. The comparative experimental results corroborate that the proposed, developed and extended mathematical model is capable of denoising and preserving the significant features in ultrasound towards better accuracy, precision and examination within the framework of biomedical imaging and other related medical, clinical, and image-signal related applied as well as computational processes.

Image reconstruction using UNET-transformer network for fast and low-dose PET scans

IntroductionLow-dose and fast PET imaging (low-count PET) play a significant role in enhancing patient safety, healthcare efficiency, and patient comfort during medical imaging procedures. To achieve high-quality images with low-count PET scans, effective reconstruction models are crucial for denoising and enhancing image quality. The main goal of this paper is to develop an effective and accurate deep learning-based method for reconstructing low-count PET images, which is a challenging problem due to the limited amount of available data and the high level of noise in the acquired images. The proposed method aims to improve the quality of reconstructed PET images while preserving important features, such as edges and small details, by combining the strengths of UNET and Transformer networks. Material and methodsThe proposed TrUNET-MAPEM model integrates a residual UNET-transformer regularizer into the unrolled maximum a posteriori expectation maximization (MAPEM) algorithm for PET image reconstruction. A loss function based on a combination of structural similarity index (SSIM) and mean squared error (MSE) is utilized to evaluate the accuracy of the reconstructed images. The simulated dataset was generated using the Brainweb phantom, while the real patient dataset was acquired using a Siemens Biograph mMR PET scanner. We also implemented state-of-the-art methods for comparison purposes: OSEM, MAPOSEM, and supervised learning using 3D-UNET network. The reconstructed images are compared to ground truth images using metrics such as peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and relative root mean square error (rRMSE) to quantitatively evaluate the accuracy of the reconstructed images. ResultsOur proposed TrUNET-MAPEM approach was evaluated using both simulated and real patient data. For the patient data, our model achieved an average PSNR of 33.72 dB, an average SSIM of 0.955, and an average rRMSE of 0.39. These results outperformed other methods which had average PSNRs of 36.89 dB, 34.12 dB, and 33.52 db, average SSIMs of 0.944, 0.947, and 0.951, and average rRMSEs of 0.59, 0.49, and 0.42. For the simulated data, our model achieved an average PSNR of 31.23 dB, an average SSIM of 0.95, and an average rRMSE of 0.55. These results also outperformed other state-of-the-art methods, such as OSEM, MAPOSEM, and 3DUNET-MAPEM. The model demonstrates the potential for clinical use by successfully reconstructing smooth images while preserving edges. The comparison with other methods demonstrates the superiority of our approach, as it outperforms all other methods for all three metrics. ConclusionThe proposed TrUNET-MAPEM model presents a significant advancement in the field of low-count PET image reconstruction. The results demonstrate the potential for clinical use, as the model can produce images with reduced noise levels and better edge preservation compared to other reconstruction and post-processing algorithms. The proposed approach may have important clinical applications in the early detection and diagnosis of various diseases.

A fractional-order image segmentation model with application to low-contrast and piecewise smooth images

In this paper, we propose a two-stage image segmentation model based on structure tensor and fractional-order regularization. In the first stage, we use the fractional-order regularization to approximate the Hausdorff measure of the Mumford-Shah (MS) model. The existence and uniqueness of the solution is proved and the alternating direction implicit (ADI) scheme is used to find the solution of the modified MS model. In the second stage, a thresholding is used to induce the segmentation of the target. The superior performances of the proposed model are demonstrated by some comparative experimental results with several state-of-art methods.

Determining the Optimal Energy Level of Virtual Monoenergetic Images in Dual-Source CT for Diagnosis of Bowel Obstruction and Colitis

Images from 64 patients undergoing an enhanced abdominal-pelvis scan at portal phase in dual-energy CT mode for the diagnosis of colitis or bowel obstruction were retrospectively analyzed. Acquisitions were performed on a third-generation dual-source CT (DSCT) 100/Sn150 kVp. Mixed images were generated, as well as virtual monoenergetic images (VMIs) at 40/50/60/70 keV. Objective image quality was assessed on VMIs and mixed images by measuring contrast, noise and contrast-to-noise ratio (CNR). Noise, smoothing and overall image quality were subjectively analyzed by two radiologists using Likert scales. For both patient groups, the noise decreased significantly according to the energy level from 40 to 60 keV by −47.2 ± 24.0% for bowel obstruction and −50.4 ± 18.2% for colitis. It was similar between 60 and 70 keV (p = 0.475 and 0.059, respectively). Noise values were significantly higher in VMIs than in mixed images, except for 70 keV (p = 0.53 and 0.071, respectively). Similar results were observed for contrast values, with a decrease between 40 and 70 keV of −56.3 ± 7.9% for bowel obstruction −56.2 ± 10.9% for colitis. The maximum CNR value was found at 60 keV compared to other energy levels and mixed images, but there was no significant difference with the other energy levels apart from 70 keV (−9.7 ± 9.8%) for bowel obstruction and 40 keV (−6.6 ± 8.2%) and 70 keV (−5.8 ± 9.2%) for colitis. The VMIs at 60 keV presented higher scores for all criteria for bowel obstruction and colitis, with no significant difference in smoothing score compared to mixed images (p = 0.119 and p = 0.888, respectively).

Weighted and truncated $$L_1$$ image smoothing based on unsupervised learning

Weighted and truncated $$L_1$$ image smoothing based on unsupervised learning

Optimal Solution for Segmentation of Malignant Melanoma Dermoscopic Images

Melanoma Malignant (MM) is the most common and dangerous form of skin cancer, which is analyzed by using Dermoscopic images in computer sciences. Segmentation technique is used to separate lesion part from healthy part in Dermoscopic images. In this research, comparison of different most popular segmented Dermoscopic image technique like Type-2 Fuzzy, Hybrid Threshold, Wavelet, Gradient Vector Flow (GVF), and Watershed etc. is approached and then better segmentation technique is proposed. In these segmentation techniques different issues like problem of hair, different color lesion, specular reflection and smoothing transaction between lesion and skin were not taken under consideration. Our methodology involves three levels of hierarchy. In the preprocessing step, it deals with problem of hair, bubble noise, smoothing and reflection noise in Dermoscopic images. These noise removals are achieved by using different filters like “Derivative of Gaussian filter and Bootomhat filter”. After region of interest is extracted then combination of threshold, image enhancement and morphological filter are used to produce the efficient algorithm for segmentation. At the end step, segmented crop image is compared with dice coefficient and experimental results of gross error rate are evaluated. For this purpose, PH² Dataset is used that contains 200 Dermoscopic images with the lesion images. The lesion images are extracted by the expert dermatologists.