Quality Of Original Image Research Articles

CABnet: A channel attention dual adversarial balancing network for multimodal image fusion

Infrared and visible image fusion aims to generate informative images by leveraging the distinctive strengths of infrared and visible modalities. These fused images play a crucial role in subsequent downstream tasks, including object detection, recognition, and segmentation. However, complementary information is often difficult to extract. Existing generative adversarial network-based methods generate fused images by modifying the distribution of source images' features to preserve instances and texture details in both infrared and visible images. Nevertheless, these approaches may result in a degradation of the fused image quality when the original image quality is low. Considering the balance of information from different modalities can improve the quality of the fused image. Hence, we introduce CABnet, a Channel Attention dual adversarial Balancing network. CABnet incorporates a channel attention mechanism to capture crucial channel features, thereby, enhancing complementary information. It also employs an adaptive factor to control the mixing distribution of infrared and visible images, which ensures the preservation of instances and texture details during the adversarial process. To enhance efficiency and reduce reliance on manual labeling, our training process adopts a semi-supervised learning strategy. Through qualitative and quantitative experiments across multiple datasets, CABnet surpasses existing state-of-the-art methods in fusion performance, notably achieving a 51.3% enhancement in signal to noise ratio and a 13.4% improvement in standard deviation.

Cutting through the noise: A Three-Way Comparison of Median, Adaptive Median, and Non-Local Means Filter for MRI Images

Medical Imaging is an essential practice in radiology to create high-standard images of the human brain. In medical imaging, denoising techniques are essential during image processing for a meaningful view of the anatomical structure of the images. In order to overcome the denoising issues, various filtering techniques and smoothening algorithms have come forth to get an accurate image for better diagnosis while preserving the original image quality. This work utilizes three computational methods for filtering noise that could distort the factual information in MRI images. The input used as the data throughout this study are MR images in grayscale contaminated with Salt and pepper noise, the most common noise in MRI images. To de-noise, a comparative analysis of three specific filters, namely the Non-Local Means filter, Median filter, and Adaptive Median filter, is conducted to do a study that gives the best results among them at different noise densities. Peak Signal-To-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are utilized as the main components to examine the behavior of the suggested filters in this study. The results show that at every value of noise density, i.e., 0.1, 0.3, 0.6, the adaptive median filter gives the highest average PSNR of 42.04, 34.36, and 28.10 and average SSIM of 0.97, 0.95, and 0.91, respectively. Hence, it indicates that the adaptive median filter outperforms the other two filters regarding PSNR and SSIM.

Solving photogrammetric cold cases using AI-based image matching: New potential for monitoring the past with historical aerial images

With the ongoing digitization in archives, an increasing number of historical data becomes available for research. This includes historical aerial images which provide detailed information about the depicted area. Among the applications enabled by these images are change detection of land use, land cover, glaciers, and coastal environments as well as the observation of land degradation, and natural hazards. Studying the depicted areas and occurring 3D deformations requires the generation of a digital surface model (DSM) which is usually obtained via photogrammetric Structure-from-Motion (SfM). However, conventional SfM workflows often fail in registering historical aerial images due to their radiometric characteristics introduced by digitization, original image quality, or vast temporal changes between epochs. We demonstrate that the feature matching step in the Structure from Motion (SfM) pipeline is particularly crucial. To address this issue, we apply the two synergetic neural network methods SuperGlue and DISK, improving feature matching for historical aerial images. This requires several modifications to enable rotational invariance and leveraging the high resolution of aerial images. In contrast to other studies our workflow does not require any prior information such as DSMs, flight height, focal lengths, or scan resolution which are often no more extent in archives. It is shown that our methods using adapted parameter settings are even able to deal with quasi texture-less images. This enables the simultaneous processing of various kind of mono-temporal and multi-temporal data handled in a single workflow from data preparation over feature matching through to camera parameter estimation and the generation of a sparse point cloud. It outperforms conventional strategies in the number of correct feature matches, number of registered images and calculated 3D points and allows the generation of multi-temporal DSMs with high quality.With the flexibility of the method, it enables the automatic processing of formerly unusable or only to be interactively processed data, e.g. aerial images where the flight route is unknown, or with difficult radiometric properties. This makes it possible to go back even further in time, where the data quality usually decreases, and enables a holistic monitoring and comparison of environments of high interest. The code is made publicly available at https://github.com/tudipffmgt/HAI-SFM.

2D compressed sensing of encrypted images based on complex‐valued measurement matrix

AbstractWhen using untrusted third parties to compress and transmit images in real‐life scenarios, it is vital to encrypt them before compression. In order to better address the issues of low security in the original image and poor reconstruction quality of the encrypted image during compressed sensing, this paper proposes a 2D compressed sensing scheme for encrypted images based on complex‐valued measurement matrix (2DCS‐CVM). Firstly, the SHA‐256 algorithm generates keys for the hyperchaotic Lorenz system, and then the chaotic sequences are used to create encrypted images with increased security through subtractive diffusion and global permutation. Secondly, the complex‐valued Vandermonde measurement matrix is utilized for 2D compressed sensing on the encrypted image, and the two‐dimensional projected gradient with embedding decryption algorithm is used to generate recovered images with improved reconstruction performance. Finally, the measurement matrix's computational complexity and transmission bandwidth are reduced through structural sparsification with sparse random matrices. Simulation results demonstrate that this scheme offers an optimal balance between storage, computational complexity, hardware implementation, and reconstruction performance while providing excellent security and robustness.

Newly Designed Identification Scheme for Monitoring Ice Thickness on Power Transmission Lines

Overhead power transmission line icing (PTLI) disasters are one of the most severe dangers to power grid safety. Automatic iced transmission line identification is critical in various fields. However, existing methods primarily focus on the linear characteristics of transmission lines, employing a two-step process involving edge and line detection for PTLI identification. Nonetheless, these traditional methods are often complicated when confronted with challenges such as background noise or variations in illumination, leading to incomplete identification of the target area, missed target regions, or misclassification of background pixels as foreground. This paper proposes a new iced transmission line identification scheme to overcome this limitation. In the initial stage, we integrate the image restoration method with image filter enhancement to restore the image’s color information. This combined approach effectively retains valuable information and preserves the original image quality, thereby mitigating the noise presented during the image acquisition. Subsequently, in the second stage, we introduce an enhanced multi-threshold algorithm to separate background and target pixels. After image segmentation, we enhance the image and obtain the region of interest (ROI) through connected component labeling modification and mathematical morphology operations, eliminating background regions. Our proposed scheme achieves an accuracy value of 97.72%, a precision value of 96.24%, a recall value of 86.22%, and a specificity value of 99.48% based on the average value of test images. Through object segmentation and location, the proposed method can avoid background interference, effectively solve the problem of transmission line icing identification, and achieve 90% measurement accuracy compared to manual measurement on the collected PTLI dataset.

Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area

Rapid glacier mass loss in high-alpine catchments exposes unconsolidated sediments to erosion by meltwater-fed mountain streams, with impact on the catchment-scale sediment dynamics. Proglacial outwash areas (OWA) are a key spatial element in this sediment flux cascade, with grain size distributions (GSD) as an integral variable in current and future glacio-fluvial processes and associated effects.In this study, surface grain size estimation methods based on linear correlations between geometric surface roughness and GSD data are adapted to test this new mapping approach for areal GSD patterns and their changes in a proglacial OWA. Surface roughness is computed with adaptive kernel sizes from high-resolution topographic point clouds derived from UAV-based photogrammetry. For model calibration, ground truth data is manually obtained from 30 dry-exposed GSD sampling patches of 1 m2 each. The novelty and potential of this surface roughness regression model lies in its capability to robustly map GSD percentiles at 1-m cell resolution in highly dynamic proglacial environments and inherent data scarcity, drastically reducing the portion of labour-intensive and time-consuming manual surveying.Our optimized linear regression function for the key figure D84, which is widely used in practice, returns a coefficient of determination of 76.4 %. The linear relation found between the D84 of field GSD and the 84th distribution percentile of surface roughness metrics has a slope of α = 6.01, which is consistent with the range of recently published studies using LiDAR-based and photogrammetric surveys. The presented surface roughness regression model provides realistic surficial estimations of areal GSD patterns in a proglacial domain of sediment remobilization. In addition, this work analyses and discusses detected changes in the estimated D84 over a three-month period. A systematic shift is identified for sediment compositions with an initial D84 above 60 mm. This is thought to be an effect of the original image quality and variable lighting conditions between photogrammetric surveys and indicates that best practice should include careful survey planning for most uniform flight settings. Nevertheless, visually verified significant changes in D84 along frequently wet low-flow channels can be detected automatically, and thus the findings demonstrate the feasibility of this GSD monitoring approach for tracking fluvial re-working of surface sediment.

Geometric and Radiometric Quality Assessments of UAV-Borne Multi-Sensor Systems: Can UAVs Replace Terrestrial Surveys?

Unmanned aerial vehicles (UAVs), also known as drones, are a cost-effective alternative to traditional surveying methods, and they can be used to collect geospatial data over inaccessible or hard-to-reach locations. UAV-integrated miniaturized remote sensing sensors such as hyperspectral and LiDAR sensors, which formerly operated on airborne and spaceborne platforms, have recently been developed. Their accuracies can still be guaranteed when incorporating pieces of equipment such as ground control points (GCPs) and field spectrometers. This study conducted three experiments for geometric and radiometric accuracy assessments of simultaneously acquired RGB, hyperspectral, and LiDAR data from a single mission. Our RGB and hyperspectral data generated orthorectified images based on direct georeferencing without any GCPs. Because of this, a base station is required for the post-processed Global Navigation Satellite System/Inertial Measurement Unit (GNSS/IMU) data. First, we compared the geometric accuracy of orthorectified RGB and hyperspectral images relative to the distance of the base station to determine which base station should be used. Second, point clouds could be generated from overlapped RGB images and a LiDAR sensor. We quantitatively and qualitatively compared RGB and LiDAR point clouds in this experiment. Lastly, we evaluated the radiometric quality of hyperspectral images, which is the most critical factor of the hyperspectral sensor, using reference spectra that was simultaneously measured by a field spectrometer. Consequently, the distance of the base station for post-processing the GNSS/IMU data was found to have no significant impact on the geometric accuracy, indicating that a dedicated base station is not always necessary. Our experimental results demonstrated geometric errors of less than two hyperspectral pixels without using GCPs, achieving a level of accuracy that is comparable to survey-level standards. Regarding the comparison of RGB- and LiDAR-based point clouds, RGB point clouds exhibited noise and lacked details; however, through the cleaning process, their vertical accuracy was found to be comparable with LiDAR’s accuracy. Although photogrammetry generated denser point clouds compared with LiDAR, the overall quality for extracting the elevation data greatly relies on factors such as the original image quality, including the image’s occlusions, shadows, and tie-points, for matching. Furthermore, the image spectra derived from hyperspectral data consistently demonstrated high radiometric quality without the need for in situ field spectrum information. This finding indicates that in situ field spectra are not always required to guarantee the radiometric quality of hyperspectral data, as long as well-calibrated targets are utilized.

A Novel Approach for Hybrid Image Segmentation GCPSO: FCM Techniques for MRI Brain Tumour Identification and Classification.

In recent times, the early detection of brain tumour analysis and classification has become a very vital part of the medical field. The MRI scan image is the most significant tool to study brain tissue for proper diagnosis and efficient treatment planning to detect the early stages. In this research study, the two contributions were executed in the preprocessing mode. (a) Using wavelet transform to apply decomposed sub-bands of a low-frequency signal to control and adapt the spatial and intensity parameters in a bilateral filter and (b) to detect texture regions and block boundary to control and adapt the spatial and intensity parameters in a bilateral filter When compared to other image resolution methods, the adaptive bilateral method restores the original image quality and has a higher accuracy rate. Using the hybrid segmentation method of GCPSO (Guaranteed Convergence Particle Swarm Optimization) -FCM (Fuzzy C-Mean) techniques, the results were compared with various segmentation. The proposed segmentation gives a better accuracy rate of 95.32%.

RD-IWAN: Residual Dense Based Imperceptible Watermark Attack Network

Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility.

Development of a Super-Resolution Scheme for Pediatric Magnetic Resonance Brain Imaging Through Convolutional Neural Networks

Pediatric medical imaging represents a real challenge for physicians, as children who are patients often move during the examination, and it causes the appearance of different artifacts in the images. Thus, it is not possible to obtain good quality images for this target population limiting the possibility of evaluation and diagnosis in certain pathological conditions. Specifically, magnetic resonance imaging (MRI) is a technique that requires long acquisition times and, therefore, demands the use of sedation or general anesthesia to avoid the movement of the patient, which is really damaging in this specific population. Because ALARA (as low as reasonably achievable) principles should be considered for all imaging studies, one of the most important reasons for establishing novel MRI imaging protocols is to avoid the harmful effects of anesthesia/sedation. In this context, ground-breaking concepts and novel technologies, such as artificial intelligence, can help to find a solution to these challenges while helping in the search for underlying disease mechanisms. The use of new MRI protocols and new image acquisition and/or pre-processing techniques can aid in the development of neuroimaging studies for children evaluation, and their translation to pediatric populations. In this paper, a novel super-resolution method based on a convolutional neural network (CNN) in two and three dimensions to automatically increase the resolution of pediatric brain MRI acquired in a reduced time scheme is proposed. Low resolution images have been generated from an original high resolution dataset and used as the input of the CNN, while several scaling factors have been assessed separately. Apart from a healthy dataset, we also tested our model with pathological pediatric MRI, and it successfully recovers the original image quality in both visual and quantitative ways, even for available examples of dysplasia lesions. We hope then to establish the basis for developing an innovative free-sedation protocol in pediatric anatomical MRI acquisition.

Efficient methods for digital image watermarking and information embedding

Now a day to achieve security of digital data, utilizing digital image watermarking scheme is the option. The primary purpose to upgrade image watermarking scheme is to fulfill the need of sufficient imperceptibility, capacity, and robustness of watermark against several attacks like JPEG compression parameter, salt and pepper noise, cropping factor of the image part which contains a watermark and rotating the watermark at one particular angle. During embedding stage in digital image watermarking scheme, a secret message or image introduced into a host image without destroying the quality of original cover image for security and authentication of the data. During extraction stage in the watermarking scheme applying a process to that employed in insertion step but in a reverse way. In this paper different evaluation based on the watermarking methods described. Many projects have been presented such as DCT, DWT, DFT, SVD using FWHT, QIM, BPNN, DFIS, EZW, Huffman coding to provide protection, robustness and high embedding capacity. In the digital image watermarking world, where Peak signal-to-noise ratio (PSNR) is utilized to determine the image quality factor and Normalized correlation (NC) and robustness of the watermark. Using this learning opportunity an environment friendly digital image watermarking scheme can be used with sustainable infrastructure in developing countries.

On improving the performance of glitch classification for gravitational wave detection by using Generative Adversarial Networks

ABSTRACT Spectrogram classification plays an important role in analysing gravitational wave data. In this paper, we propose a framework to improve the classification performance by using Generative Adversarial Networks (GANs). As substantial efforts and expertise are required to annotate spectrograms, the number of training examples is very limited. However, it is well known that deep networks can perform well only when the sample size of the training set is sufficiently large. Furthermore, the imbalanced sample sizes in different classes can also hamper the performance. In order to tackle these problems, we propose a GAN-based data augmentation framework. While standard data augmentation methods for conventional images cannot be applied on spectrograms, we found that a variant of GANs, ProGAN, is capable of generating high-resolution spectrograms which are consistent with the quality of the high-resolution original images and provide a desirable diversity. We have validated our framework by classifying glitches in the Gravity Spy data set with the GAN-generated spectrograms for training. We show that the proposed method can provide an alternative to transfer learning for the classification of spectrograms using deep networks, i.e. using a high-resolution GAN for data augmentation instead. Furthermore, fluctuations in classification performance with small sample sizes for training and evaluation can be greatly reduced. Using the trained network in our framework, we have also examined the spectrograms with label anomalies in Gravity Spy.

Denoising Terahertz Image Using Non-Linear Filters

This work was supported by the National Natural Science Foundation of China, (Grant No. 11872058), the Sichuan Science and Technology Program of China (No.2019YFG0114) Abstract A major challenge in the processing and analysis of images is the presence of noise especially in terahertz images. Denoising techniques are designed to remove noise or distorted images while maintaining the original image quality. In this paper, terahertz image denoising is proposed using different filtering methods. This work provides an algorithm that denotes the terahertz image with a non- linear and spatial function of mean and medium filter. The THz images were transformed using Gaussian and Salt & Pepper noise at different percentage levels from 5% to 50%. Universal Image Quantitative Analysis was utilized specifically the most renowned PSNR, MSE, MAE and IEF were explored in this work. Experimental results show that the mean-median filter outperforms Salt and Pepper in removing the Gaussian noise especially when noise factor is increasing. Keywords: Mean- median filter, terahertz image, Gaussian noise, Salt & pepper noise DOI: 10.7176/CEIS/12-2-02 Publication date: April 30 th 2021

Generation of Brain Dual-Energy CT from Single-Energy CT Using Deep Learning.

Deep learning (DL) has shown great potential in conversions between various imaging modalities. Similarly, DL can be applied to synthesize a high-kV computed tomography (CT) image from its corresponding low-kV CT image. This indicates the feasibility of obtaining dual-energy CT (DECT) images without purchasing a DECT scanner. In this study, we investigated whether a low-to-high kV mapping was better than a high-to-low kV mapping. We used a U-Net model to perform conversions between different kV CT images. Moreover, we proposed a double U-Net model to improve the quality of original single-energy CT images. Ninety-eight patients who underwent brain DECT scans were used to train, validate, and test the proposed DL-based model. The results showed that the low-to-high kV conversion was better than the high-to-low kV conversion. In addition, the DL-based DECT images had better signal-to-noise ratios (SNRs) than the true (original) DECT images, but at the expense of a slight loss in spatial resolution. The mean CT number differences between the true and DL-based DECT images were within [Formula: see text]1 HU. No statistically significant difference in CT number measurements was found between the true and DL-based DECT images (p>0.05). The DL-based DECT images with improved SNR could produce low-noise virtual monoenergetic images. Our preliminary results indicate that DL has the potential to generate brain DECT images using single-energy brain CT images.

Embedding electronic patient information in clinical images: an improved and efficient reversible data hiding technique

Reversible Data Hiding (RDH) is one of the popular and highly recommended methods to enhance medical data security or Electronic Patient Record (EPR) privacy. In medical field, care must be taken to maintain the confidentiality of information within the clinical images without causing any degradation to the original quality of the medical image, which corresponds to the diagnosis of a patient. In this presented approach, we introduce a variation to the RDH scheme, which enables higher data capacity to be embedded without causing any significant impact on the quality of medical images. Rhombus Mean Interpolation technique is employed for predicting the interpolated points in the cover image instead of Pixel To Block (PTB) conversion procedure. We used the data related to the patient diagnosis as the secret information to be embedded in medical images, checksum for 2 x 2 non-overlapping block is also embedded for tamper detection and content authentication at the receiver. Validated the quality of the marked image after integration, in terms of PSNR and SSIM metrics. It is observed to have significant improvement in Embedding capacity up to 3 bpp and better image quality compared to the existing schemes.

Evaluating biometrics fingerprint template protection for an emergency situation

Biometric template protection approaches have been developed to secure the biometric templates against image reconstruction on the stored templates. Two cancellable fingerprint template protection approaches namely minutiae-based bit-string cancellable fingerprint template and modified minutiae-based bit-string cancellable fingerprint template, are selected to be evaluated. Both approaches include the geometric information of the fingerprint into the extracted minutiae. Six modified fingerprint data sets are derived from the original fingerprint images in FVC2002DB1_B and FVC2002DB2_B by conducting the rotation and changing the quality of original fingerprint images according to the environment conditions during an emergency situation such as wet or dry fingers and disoriented angle of fingerprint images. The experimental results show that the modified minutiae-based bit-string cancellable fingerprint template performs well on all conditions during an emergency situation by achieving the matching accuracy between 83% and 100% on FVC2002DB1_B data set and between 99% and 100% on FVC2002DB2_B data set.

A New Approach of Image Compression Techniques in Image Processing

In this research work to present the approach for image compression. The different techniques increasing attraction and dependence on the digital photograph have specified increase to that applying visual communication. That approach has to require a huge amount of information in the form of data. For the information, the transformation required less image size. The number of researches has been proposed approach for image compression but that approach destroys the original image quality. To propose an approach for image compression with the help of lossless and lossy.

AutoCryoPicker: an unsupervised learning approach for fully automated single particle picking in Cryo-EM images

BackgroundAn important task of macromolecular structure determination by cryo-electron microscopy (cryo-EM) is the identification of single particles in micrographs (particle picking). Due to the necessity of human involvement in the process, current particle picking techniques are time consuming and often result in many false positives and negatives. Adjusting the parameters to eliminate false positives often excludes true particles in certain orientations. The supervised machine learning (e.g. deep learning) methods for particle picking often need a large training dataset, which requires extensive manual annotation. Other reference-dependent methods rely on low-resolution templates for particle detection, matching and picking, and therefore, are not fully automated. These issues motivate us to develop a fully automated, unbiased framework for particle picking.ResultsWe design a fully automated, unsupervised approach for single particle picking in cryo-EM micrographs. Our approach consists of three stages: image preprocessing, particle clustering, and particle picking. The image preprocessing is based on multiple techniques including: image averaging, normalization, cryo-EM image contrast enhancement correction (CEC), histogram equalization, restoration, adaptive histogram equalization, guided image filtering, and morphological operations. Image preprocessing significantly improves the quality of original cryo-EM images. Our particle clustering method is based on an intensity distribution model which is much faster and more accurate than traditional K-means and Fuzzy C-Means (FCM) algorithms for single particle clustering. Our particle picking method, based on image cleaning and shape detection with a modified Circular Hough Transform algorithm, effectively detects the shape and the center of each particle and creates a bounding box encapsulating the particles.ConclusionsAutoCryoPicker can automatically and effectively recognize particle-like objects from noisy cryo-EM micrographs without the need of labeled training data or human intervention making it a useful tool for cryo-EM protein structure determination.

Gradient-aware blind face inpainting for deep face verification

ID face photos are widely used for identity verification in many business authentication situations. To avoid any infringement and misuse, the ID photos provided by the relevant government agencies and business organizations are always corrupted with designed watermarks, such as random wave lines or meshes. These corrupted images are further compressed with JPEG algorithm to reduce their storage size. The artifacts caused by the random meshes and JPEG compression seriously destroy the original image information and quality, which makes the face verification between the corrupted ID faces and daily life images extremely difficult. To tackle these issues, a preprocessing step called blind inpainting is needed to recover the corrupted ID faces. In this paper, we present a new framework to address this blind face inpainting problem. We use an improved Deep Recursive Residual Network (IDRRN) to learn an effective non-linear mapping between the corrupted and clean ID image pairs. To train the IDRRN model, a unified Euclidean loss function considering both 0- and 1st-order pixel residuals is proposed to enhance the image pixel as well as gradient reconstruction. In addition, we collect a dataset of clean ID images and develop a simulation procedure to generate corresponding corrupted ID face images. Final experiments demonstrate that the recovered ID face images inferred from our IDRRN model achieve the best performance in terms of perceptual error and verification accuracy.

A Hybrid Approach to Reduce Cone-Beam Artifacts for a Circular Orbit Cone-Beam CT System

In a cone-beam computed tomography (CT) system, the Feldkamp, Davis, and Kress (FDK) algorithm produces cone-beam artifacts due to insufficient object sampling in the missing cone region of frequency space. While total variation minimization-based iterative reconstruction (TV-IR) may reduce cone-beam artifacts by filling in the missing cone region, it introduces image blurring or noise increase depending on the regularization parameter. In this paper, we propose a method to reduce cone-beam artifacts through an optimal combination of FDK and TV-IR images. The method utilizes FDK (TV-IR) data outside (inside) the missing cone region, which enables to keep the original image quality of the FDK image and preserve the advantages of the TV-IR image for cone-beam artifact reduction. To validate the proposed method, we used numerical phantoms composed of Defrise disks, vertical plates, and star objects and assessed the image quality of FDK, TV-IR, and the proposed method using the mean squared error, contrast to noise ratio, and structural similarity with different TV-IR regularization parameters. Experimental validation was also conducted using a spine phantom with a bench-top cone-beam CT system. The results showed that the performance of the cone-beam artifacts reduction in TV-IR depended on the value of the regularization parameter, which often produced suboptimal image quality for different imaging tasks. However, the proposed method provided good image quality regardless of the regularization parameter values.