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With technological advancements, particularly in image analysis, phenotyping can now be conducted more accurately, impartially, and non-destructively. However, distortions caused by different camera angles as well as environmental factors, such as lighting, lead to inaccurate results in image analysis. Therefore, a method for correcting these distortions is necessary to achieve more precise outcomes. The objective of this study was to develop an algorithm that corrects image distortion and improves tomato fruit phenotyping and to determine its efficiency. A photographic studio and a smartphone were used to capture the images. To test the developed algorithm, twelve 4 × 4 cm black squares were printed on A4 sheets, with four of these sheets placed inside the studio. Additionally, eight 3 × 3 cm yellow square sheets were used as reference objects to correct distortions. A total of 40 images were obtained with different camera angles. A multiple regression model was then adjusted and tested for each image to obtain a correction factor for distortions caused by varying camera angles. In the test images, higher estimates for the coefficient of variation and mean squared error were observed at the edges and lower ones at the center. After correcting the images using the adjusted regression model, uniformity in the estimates was achieved. The same behavior was observed when validating the model with images of tomato fruit. The coefficient of determination of the adjusted model was over 80%, indicating a high fit for the selected model. Therefore, the image distortion correction methodology ensures more accurate results in tomato fruit phenotyping.

The recent combination of scanning electron microscopy and digital image correlation (SEM-DIC) enables the experimental investigation of full-field deformations at much smaller length scales than is possible using optical digital image correlation methods. However, the high spatial resolution of SEM-DIC comes at the cost of complex image distortions, long image scan times that can capture gradients from stress relaxation, and a high noise sensitivity to SEM parameters. In this paper, it is shown that these sources of error can significantly impact the quality of the results and must be accounted for in order to perform accurate SEM-DIC experiments. An existing framework for distortion corrections is adapted to improve accuracy and the procedures are described in detail. As the results demonstrate, time varying drift distortion is a larger problem at high magnification while spatial distortion is more problematic at low magnification. Additionally, the new use of sample-independent calibration and a method to eliminate the detrimental effects of stress relaxation in the displacement fields prior to distortion correction are introduced. The impact of SEM settings on image noise is quantified and noise minimization schemes are examined. Finally, a uniaxial tension test on coarse-grained 1100-O aluminum is used to demonstrate these techniques, where active slip planes are identified and strain localization is examined in relation to the underlying microstructure.

In this paper, we continue the research cycle on the properties of convolutional neural network-based image recognition systems and ways to improve noise immunity and robustness. Currently, a popular research area related to artificial neural networks is adversarial attacks. The adversarial attacks on the image are not highly perceptible to the human eye, and they also drastically reduce the neural network’s accuracy. Image perception by a machine is highly dependent on the propagation of high frequency distortions throughout the network. At the same time, a human efficiently ignores high-frequency distortions, perceiving the shape of objects as a whole. We propose a technique to reduce the influence of high-frequency noise on the CNNs. We show that low-pass image filtering can improve the image recognition accuracy in the presence of high-frequency distortions in particular, caused by adversarial attacks. This technique is resource efficient and easy to implement. The proposed technique makes it possible to measure up the logic of an artificial neural network to that of a human, for whom high-frequency distortions are not decisive in object recognition.

Distortions in scanning electron microscope (SEM) images compromise characterization accuracy and restrict reliable quantitative analysis. Quantifying and correcting these distortions remains challenging due to the complexity of their inherent sources, such as scanning coil hysteresis and electronic circuit response delays. To address this, we independently developed a scanning controller and software system that enables customizable scanning strategies and is crucial for capturing unprocessed raw data. We utilized the characteristic row misalignment of snake scanning to split images into sub-images, measure offsets using the ORB algorithm, and apply pixel compensation. Experimental validation shows that corrected images exhibit reduced distortion artifacts, with structural similarity comparable to raster scanning results and improved reference-free quality metrics. The distortion magnitude is independent of magnification, primarily governed by dwell time, and stabilizes at a minimum level when the dwell time reaches a critical threshold. This work clarifies the relationship between scanning parameters and distortion behavior, guiding the optimization of SEM scanning strategies. Furthermore, it offers a potential scalable framework for distortion correction in related microscopy techniques. Many of these techniques also face distortion issues from hardware hysteresis or circuit delays, similar to SEM.

Space-surface bistatic synthetic aperture radar (SS-BiSAR) offers an additional observation angle for monostatic spaceborne SAR, making it a promising technology for high-accuracy deformation retrieval technology in local regions. Repeat-pass SS-BiSAR tomography can accurately estimate the surfaces of buildings and steep areas, effectively removing terrain phases during deformation retrieving. However, inaccuracies in the orbital ephemeris can lead to image geometry distortion, reducing image pair coherence, introducing interferometric phase errors, and consequently deteriorating tomographic precision. This paper precisely models the image geometry distortion and interferometric phase error caused by repeat-pass ephemeris error. We propose an ephemeris correction method based on the chirp-Z transform to address these issues. Furthermore, we introduce an accurate tomography model to improve 3D reconstruction accuracy. Our first SS-BiSAR tomography experiment, conducted using the Chinese Lutan-1 satellite, demonstrates that the correlation coefficient is improved by 0.16 after ephemeris error correction. Moreover, the density and precision of the tomographic point cloud are improved by 13.7% and 12.1%, respectively.

Blind image quality assessment is a challenging task particularly due to the unavailability of reference information. Training a deep neural network requires a large amount of training data which is not readily available for image quality. Transfer learning is usually opted to overcome this limitation and different deep architectures are used for this purpose as they learn features differently. After extensive experiments, we have designed a deep architecture containing two CNN architectures as its sub-units. Moreover, a self-collected image database BIQ2021 is proposed with 12,000 images having natural distortions. The self-collected database is subjectively scored and is used for model training and validation. It is demonstrated that synthetic distortion databases cannot provide generalization beyond the distortion types used in the database and they are not ideal candidates for general-purpose image quality assessment. Moreover, a large-scale database of 18.75 million images with synthetic distortions is used to pretrain the model and then retrain it on benchmark databases for evaluation. Experiments are conducted on six benchmark databases three of which are synthetic distortion databases (LIVE, CSIQ and TID2013) and three are natural distortion databases (LIVE Challenge Database, CID2013 and KonIQ-10 k). The proposed approach has provided a Pearson correlation coefficient of 0.8992, 0.8472 and 0.9452 subsequently and Spearman correlation coefficient of 0.8863, 0.8408 and 0.9421. Moreover, the performance is demonstrated using perceptually weighted rank correlation to indicatethe perceptual superiority of the proposed approach. Multiple experiments are conducted to validate the generalization performance of the proposed model by training on different subsets of the databases and validating on the test subset of BIQ2021 database.

Highly divergent estimates of benthic cover of sponges have been reported for Caribbean mesophotic reefs (90–100 m) based on quadrat point-intercept data collection using 2 methods: visual surveys conducted in situ by technical divers, and analyses of photographs taken by unmanned underwater vehicles (UUVs). The second method has been criticized for potential errors from image distortion caused by variable camera angle relative to the substratum, but without a broader comparison of both methods. We find that studies that have used the UUV-based method are advantageous for a number of reasons, most importantly: (1) access to the full mesophotic zone, (2) higher sample replication, and (3) reduced likelihood of sampling bias. For tech diving surveys conducted at 91 m, i.e. the deepest depth reported using this method but only midway through the mesophotic zone, studies have reported particularly high sponge cover (∼80 vs. <10% for UUV-based surveys), which may be a consequence of low replication and targeted sampling influenced by very short working times under hazardous conditions. When evaluating benthic abundance metrics from photographs, issues associated with variable substratum angle are common to any topographically complex surface, particularly within a quadrat. Nevertheless, point-intercept estimates are not dependent on quadrat area and are not subject to error due to image distortion or surface complexity. Unlike visual census data from tech dives, UUV photographs can be validated by taxonomic experts and archived for re-analysis. Past tech diving surveys should be repeated using the UUV-based method with greater replication over the full range of the mesophotic zone in order to reconcile divergent estimates of benthic cover.

Remote sensing imagery enables object detection systems to localize and classify targets for critical applications like surveillance and autonomous driving. However, distortions introduced during image acquisition, transmission, or compression degrade the detection performance, posing challenges for real-world applications. This study conducts a comprehensive robustness evaluation of seven state-of-the-art YOLO models, including YOLOv5, YOLOv7, YOLOv8, YOLOv9, YOLOv10, YOLOv11, and the modified YOLOv5 against four common distortions: Additive White Gaussian Noise (AWGN), JPEG and JPEG2000 compressions, and Gaussian blurring. Using the DOTA-v1.0 dataset, we generated 40 distortion test sets (10 levels per distortion type). The obtained results demonstrate that all distortions degrade performance across all evaluated models. YOLOv9 outperforms others YOLO models in terms of mean average precision under different distortions. YOLOv7 and YOLOv10 exhibit the weakest robustness, whereas YOLOv11 shows low resistance to AWGN distortion.

Tonini, A., Painho, M., & Castelli, M. (2022). Estimation of Human Body Height Using Consumer-Level UAVs. Remote Sensing, 14(23), 1-21. [6176]. https://doi.org/10.3390/rs14236176 --- This study was supported by grant DSAIPA/DS/0113/2019 from FCT (Fundação para a Ciência e a Tecnologia), Portugal. This work was also supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project—UIDB/04152/2020—Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS. Consumer-level UAVs are often employed for surveillance, especially in urban areas. Within this context, human recognition via estimation of biometric traits, like body height, is of pivotal relevance. Previous studies confirmed that the pinhole model could be used for this purpose, but only if the accurate distance between the aerial camera and the target is known. Unfortunately, low positional accuracy of the drones and the difficulties of retrieving the coordinates of a moving target like a human may prevent reaching the required level of accuracy. This paper proposes a novel solution that may overcome this issue. It foresees calculating the relative altitude of the drone from the target by knowing only the ground distance between two points visible in the image. This relative altitude can be then used to calculate the target-to-camera distance without using the coordinates of the drone or the target. The procedure was verified with real data collected with a quadcopter, first considering a controlled environment with a wooden pole of known height and then a person in a more realistic scenario. The verification confirmed that a high level of accuracy can be reached, even with regular market drones.

Recently, with more portable diagnostic devices being moved to people anywhere, point-of-care (PoC) imaging has become more convenient and more popular than the traditional “bed imaging”. Instant image segmentation, as an important technology of computer vision, is receiving more and more attention in PoC diagnosis. However, the image distortion caused by image preprocessing and the low resolution of medical images extracted by PoC devices are urgent problems that need to be solved. Moreover, more efficient feature representation is necessary in the design of instant image segmentation. In this paper, a new feature representation considering the relationships among local features with minimal parameters and a lower computational complexity is proposed. Since a feature window sliding along a diagonal can capture more pluralistic features, a Diagonal-Axial Multi-Layer Perceptron is designed to obtain the global correlation among local features for a more comprehensive feature representation. Additionally, a new multi-scale feature fusion is proposed to integrate nonlinear features with linear ones to obtain a more precise feature representation. Richer features are figured out. In order to improve the generalization of the models, a dynamic residual spatial pyramid pooling based on various receptive fields is constructed according to different sizes of images, which alleviates the influence of image distortion. The experimental results show that the proposed strategy has better performance on instant image segmentation. Notably, it yields an average improvement of 1.31% in Dice than existing strategies on the BUSI, ISIC2018 and MoNuSeg datasets.