Blind image quality evaluation metrics design for UAV photographic application

A design method of an information processing system for the multi-rotor unmanned aerial vehicle (UAV)-based earthquake rescue application is proposed. As a kind of new rescue measurement in the third medical center of the People’s Liberation Army (PLA) general hospital, this system is designed for the UAV of calamity rescue team. Four key functions are developed: the environment perception-based image enhancement, the homography matrix estimation-based image mosaicing, the imaging earthquake damage degree (EDD) evaluation, and the rescue plan recommendation. The blind image quality evaluation metrics are utilized to percept the changes of imaging environment. The homography matrix is employed to carry out the image mosaicing. Lots of image texture features are computed to assess the imaging EDD. The rescue plan is recommended by the backpropagation neural network (BPNN) with the considerations of EDD and other factors such as the population size or the weather condition. The practical tests have verified the effectiveness of the proposed system and method.

The goal of this paper is to investigate and evaluate image quality, based on quality metrics and visual perception in ultrasound imaging under different imaging conditions. We first generate and simulate Bmode ultrasound images of various objects, using Field-II simulation toolbox [1]. Then we implement and embed front-end functional modules, mid-end functions including beamforming, receive beamforming, envelop detection and log compression, and back-end image processing methods (filtering and image enhancement techniques). Ultrasound images are evaluated as pairs using various image quality evaluation metrics and visual perception evaluation. The experimental results of this study show that: (1) better image quality is significantly obtained using over-sampling and signal emphasizing (high-signal level); (2) normalized images with speckle filters are rated better in terms of index-quality; and (3) enhanced images show better visual perception on all simulation datasets. This paper shows the utility of our MATLAB test-bench favoring simulated image quality and further demonstrates that the evaluation design is an important pre-processing step especially for the hardware design of ultrasound system.

In recent years, health risks concerning high-dose x-ray radiation have become a major concern in dental computed tomography (CT) examinations. Therefore, adopting low-dose computed tomography (LDCT) technology has become a major focus in the CT imaging field. One of these LDCT technologies is downsampling data acquisition during low-dose x-ray imaging processes. However, reducing the radiation dose can adversely affect CT image quality by introducing noise and artifacts in the resultant image that can compromise diagnostic information. In this paper, we propose an artifact correction method for downsampling CT reconstruction based on deep learning. We used clinical dental CT data with low-dose artifacts reconstructed by conventional filtered back projection (FBP) as inputs to a deep neural network and corresponding high-quality labeled normal-dose CT data during training. We trained a generative adversarial network (GAN) with Wasserstein distance (WGAN) and mean squared error (MSE) loss, called m-WGAN, to remove artifacts and obtain high-quality CT dental images in a clinical dental CT examination environment. The experimental results confirmed that the proposed algorithm effectively removes low-dose artifacts from dental CT scans. In addition, we showed that the proposed method is efficient for removing noise from low-dose CT scan images compared to existing approaches. We compared the performances of the general GAN, convolutional neural networks, and m-WGAN. Through quantitative and qualitative analysis of the results, we concluded that the proposed m-WGAN method resulted in better artifact correction performance preserving the texture in dental CT scanning. The image quality evaluation metrics indicated that the proposed method effectively improves image quality when used as a postprocessing technique for dental CT images. To the best of our knowledge, this work is the first deep learning architecture used with a commercial cone-beam dental CT scanner. The artifact correction performance was rigorously evaluated and demonstrated to be effective. Therefore, we believe that the proposed algorithm represents a new direction in the research area of low-dose dental CT artifact correction.

Minimum variance (MV) beamformers have been introduced in medical ultrasound imaging to improve image quality. In most cases, the MV beamformers have been investigated in terms of resolution improvement. However, the contrast-to-noise ratio (CNR) is also a clinically important metrics and gathers attention recently. In this study, we examined the diagonal loading parameter σ in MV beamforming and determined its appropriate value by evaluating image quality evaluation metrics including CNR. In order to further improve the image quality, a method for determining the value of σ based on the difference in statistical properties of received ultrasonic echo signals was also investigated. The phantom experimental results showed that the proposed method achieved a better CNR than the conventional MV beamformer while keeping resolution significantly better than that in delay-and-sum beamforming.

Ultrasound imaging is a widely used and safe medical diagnostic technique, due to its noninvasive nature, low cost, capability of forming real time imaging, and the continuing improvements in image quality. However; the usefulness of ultrasound imaging is degraded by the presence of signal dependant noise known as speckle. It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. In ultrasound (US) imaging, denoising is intended to improve quantitative image analysis techniques. In this paper, a new version of the Non Local (NL-) means filter adapted for US images is proposed based on Similarity function depend on specific characteristics of the variance speckle noise in ultrasound images. The proposed method has been compared with Median, Wavelet, Mean and variance local statistics, Geometric, Anisotropic diffusion filtering, and Non ' local means filter using quantitative parameters. From the visual results and image quality evaluation metrics obtained over real images we can conclude that the modified(NL-) means filter can be successfully used for ultrasound image denoising, and performs better results than all other methods while still retaining the structural details and retains the edges and textures very well while removing speckle noise.

It is well-known that speckle is a multiplicative noise that degrades the visual evaluation in ultrasound imaging. The recent advancements in ultrasound instrumentation and portable ultrasound devices necessitate the need of more robust despeckling techniques for enhanced ultrasound medical imaging for both routine clinical practice and teleconsultation. The objective of this work was to carry out a comparative evaluation of despeckle filtering based on texture analysis, image quality evaluation metrics, and visual evaluation by medical experts in the assessment of 440 (220 asymptomatic and 220 symptomatic) ultrasound images of the carotid artery bifurcation. In this paper a total of 10 despeckle filters were evaluated based on local statistics, median filtering, pixel homogeneity, geometric filtering, homomorphic filtering, anisotropic diffusion, nonlinear coherence diffusion, and wavelet filtering. The results of this study suggest that the first order statistics filter lsmv, gave the best performance, followed by the geometric filter gf4d, and the homogeneous mask area filter lsminsc. These filters improved the class separation between the asymptomatic and the symptomatic classes based on the statistics of the extracted texture features, gave only a marginal improvement in the classification success rate, and improved the visual assessment carried out by the two experts. More specifically, filters lsmv or gf4d can be used for despeckling asymptomatic images in which the expert is interested mainly in the plaque composition and texture analysis; and filters lsmv, gf4d, or lsminsc can be used for the despeckling of symptomatic images in which the expert is interested in identifying the degree of stenosis and the plaque borders. The proper selection of a despeckle filter is very important in the enhancement of ultrasonic imaging of the carotid artery. Further work is needed to evaluate at a larger scale and in clinical practice the performance of the proposed despeckle filters in the automated segmentation, texture analysis, and classification of carotid ultrasound imaging.

With the increasing use of CT in clinical practice, limiting CT radiation exposure to reduce potential cancer risks has become one of the important directions of medical imaging research. As the dose decreases, the reconstructed CT image will be severely degraded by projection noise. As an important method of image processing, supervised deep learning has been widely used in the restoration of low-dose CT(LDCT) in recent years. However, the normal-dose CT (NDCT) corresponding to a specific LDCT (it is regareded as the label of the LDCT, which is necessary for supervised learning) are very difficult to obtain. So that the application of supervised learning methods in LDCT reconstruction is limited. It is necessary to construct a unsupervised deep learning framework for LDCT reconstruction that does not depend on paired LDCT-NDCTdatasets. We presented an unsupervised learning framework for the transferring from the identity mapping to the low-dose recontruction task, called Marginal Distribution Adaptation in Multi-scale (MDAM). For NDCTs as source domain data, MDAM is an identity map with two parts: firstly, it establishes a dimensionality reduction mapping, which can obtain the same feature distribution from NDCTs and LDCTs; and then NDCTs is retrieved by reconstructing the image overview and details from the low-dimensional features. For the purpose of the feature transfer between source domain and target domain (LDCTs), we introduce the multi-scale feature extraction in the MDAM, and then eliminate differences in probability distributions of these multi-scale features between NDCTs and LDCTs through wavelet decomposition and domain adaptationlearning. Image quality evaluation metrics and subjective quality scores show that, as an unsupervised method, the performance of the MDAM approaches or even surpasses some state-of-the-art supervised methods. Especially, MDAM has been favorably evaluated in terms of noise suppression, structural preservation, and lesiondetection. We demonstrated that, the MDAM framework can reconstruct corresponding NDCTs from LDCTs with high accuracy, and without relying on any labeles. Moreover, it is more suitable for clinical application compared with supervised learningmethods. This article is protected by copyright. All rights reserved.

The underwater color disparity is an important cue for enhancing an underwater image. Applying the underwater color disparity indiscriminately to the entire underwater image tends to give rise to foreground-background crosstalk with either excessive foreground or insufficient background enhancement. To address the discriminativeness between underwater color disparities in foreground and background regions, we develop a discriminative underwater image enhancement method empowered by large foundation model technology. We first utilize the Segment Anything Model to generate segmentation masks, dividing the underwater image into foreground and background regions. This enables accurate foreground-background separation. Then, we conduct adaptive color compensation and fusion to improve the color histogram similarity for foreground and background regions separately. This corrects color deviations and improves contrasts in a discriminative manner that avoids the foreground-background crosstalk. Finally, we propose high-frequency edge fusion to extract high-frequency components from both the original underwater image and the fused image, and then fuse these components to obtain the final enhanced image. This eliminates blurred details arising from the discriminative processing of foreground and background regions. Our method represents the pioneering application of large foundation model technology to empower underwater image enhancement. Experimental results indicate that our method outperforms nine state-of-the-art underwater image enhancement methods in visual quality, achieves superior results across five underwater image quality evaluation metrics on three underwater image datasets, and is beneficial for practical applications such as underwater feature matching. We release our code at https://gitee.com/wanghaoupc/UIE SAM.

Pulmonary vein isolation with radiofrequency ablation (RFA) has become the most common procedure to treat Atrial Fibrillation (AF). However, current RFA lesion formation is guided only with indirect information (e.g. temperature, impedance, contact force), which does not guarantee transmural lesions. Non-transmural lesions are understood to contribute to AF recurrence. We have previously demonstrated that polarization-sensitive optical coherence tomography (PSOCT) can monitor RFA lesion transmurality in the left atrium (LA) of living swine. However, it requires expert interpretation. Here, we demonstrate quantitative image quality and lesion transmurality evaluation metrics applied to the in vivo LA RFA PSOCT lesion monitoring data.

Object detection from UAV thermal infrared images and videos using YOLO models

This paper proposes a new methodology to address the image quality problem encountered as the use of an unmanned aerial vehicle (UAV) in the field of bridge inspection increased. When inspecting a bridge, the image obtained from the UAV was degraded by various interference factors such as vibration, wind, and motion of UAV. Image quality degradation such as blur, noise, and low-resolution is a major obstacle in utilizing bridge inspection technology based on UAV. In particular, in the field of bridge inspection where damages must be accurately and quickly detected based on data obtained from UAV, these quality issues weaken the advantage of using UAVs by requiring re-take of images through re-flighting. Therefore, in this study, image quality assessment (IQA) based on local blur map (LBM) and image quality enhancement (IQE) using the variational Dirichlet (VD) kernel estimation were proposed as a solution to address the quality issues. First, image data was collected by setting different camera parameters for each bridge member. Second, a blur map was generated through discrete wavelet transform (DWT) and a new quality metric to measure the degree of blurriness was proposed. Third, for low-quality images with a large degree of blurriness, the blind kernel estimation and blind image deconvolution were performed to enhance the quality of images. In the validation tests, the proposed quality metric was applied to material image sets of bridge pier and deck taken from UAV, and its results were compared with those of other quality metrics based on singular value decomposition (SVD), sum of gray-intensity variance (SGV) and high-frequency multiscale fusion and sort transform (HiFST) methods. It was validated that the proposed IQA metric showed better classification performance on UAV images for bridge inspection through comparison with the classification results by human perception. In addition, by performing IQE, on average, 26% of blur was reduced, and the images with enhanced quality showed better damage detection performance through the deep learning model (i.e., mask and region-based convolutional neural network).

We are to present the specialized quality evaluation metrics by completely understanding the difference between the BPM, software focused on the process management by meeting the requirements for new enterprise environments, and other software products and reflecting the characteristics of process based-software based on the standard software quality evaluation metrics.

This paper discusses the algorithms of vision systems for automatic UAV (Unmanned Aerial Vehicle) landing. The basic algorithms for finding helipad and adapting the UAV control system to an autonomous UAV landing are presented. The authors consider algorithms to solve navigation problems, building block diagrams of automatic UAV control. Also, the developed algorithms make it possible to implement an automatic landing system for UAVs using technical vision systems, using various camera parameters, algorithms allowing research, as part of the autonomous navigation of unmanned aerial vehicles. The article discusses the developed algorithm that allows you to highlight certain characteristic points for visual navigation. Also, a search system for characteristic points has been developed that allows automatic UAV landing. These algorithms may be useful for the autonomous landing UAV systems. Algorithms can also be used to track a trajectory of the UAV system.

Towards a blind image quality evaluator using multi-scale second-order statistics

Accurate fault detection and diagnosis are critical components of any fault-tolerant control system, especially for unmanned aerial vehicles (UAVs) where reliability is paramount. Traditionally, both model-based and data-driven approaches have been applied for fault diagnosis. However, the increasing complexity of high-dimensional UAV systems has shifted focus toward data-driven methods, which leverage advanced classification algorithms to enhance fault identification and isolation. This study builds on this evolution by developing a sophisticated condition-based monitoring (CBM) system specifically designed for multirotor UAVs. In contrast to earlier studies that primarily relied on raw data for classifier training, this work introduces advanced preprocessing techniques and multidomain feature extraction, significantly improving the robustness and accuracy of fault detection. A comparative analysis is performed between feature-selection methods, including recursive feature elimination with cross-validation (RFECV) and variational autoencoder (VAE), to extract critical insights into UAV operational behavior. Through testing and evaluating various classification models on data from a hexarotor UAV under diverse actuator fault conditions, this research identifies optimal approaches for real-time fault detection and diagnosis. Results demonstrate notable improvements across all evaluation metrics, establishing this approach as a substantial advancement in UAV fault tolerance.