Image Distortion Research Articles

Exploring the feasibility of FOCUS DWI with deep learning reconstruction for breast cancer diagnosis: A comparative study with conventional DWI.

This study compared field-of-view (FOV) optimized and constrained undistorted single-shot diffusion-weighted imaging (FOCUS DWI) with deep-learning-based reconstruction (DLR) to conventional DWI for breast imaging. This study prospectively enrolled 49 female patients suspected of breast cancer from July to December 2023. The patients underwent conventional and FOCUS breast DWI and data were reconstructed with and without DLR. Two radiologists independently evaluated three images per patient using a 5-point Likert scale. Objective evaluations, including signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and apparent diffusion coefficient (ADC), were conducted using manual region of interest-based analysis. The subjective and objective evaluations were compared using the Friedman test. The scores for the overall image quality, anatomical details, lesion conspicuity, artifacts, and distortion in FOCUS-DLR DWI were higher than in conventional DWI (all P < 0.001). The SNR of FOCUS-DLR DWI was higher than that of conventional and FOCUS DWI (both P < 0.001), while FOCUS and conventional DWI were similar (P = 0.096). Conventional, FOCUS, and FOCUS-DLR DWI had similar CNR and ADC values. Our findings indicate that images produced by FOCUS-DLR DWI were superior to conventional DWI, supporting the applicability of this technique in clinical practice. DLR provides a new approach to optimize breast DWI.

QualityNet: A multi-stream fusion framework with spatial and channel attention for blind image quality assessment

This study introduces a novel Blind Image Quality Assessment (BIQA) approach leveraging a multi-stream spatial and channel attention model. Our method addresses challenges posed by diverse image content and distortions by integrating feature maps from two distinct backbones. Through spatial and channel attention mechanisms, our algorithm prioritizes regions of interest, enhancing its ability to capture crucial image details. Extensive evaluations on four benchmark datasets demonstrate superior performance compared to existing methods, closely aligning with human perceptual assessment. Our approach exhibits exceptional generalization capabilities on both authentic and synthetic distortion databases. Moreover, it demonstrates a distinctive focus on perceptual foreground information, enhancing its practical applicability. Thorough quantitative analyses underscore the algorithm’s superior performance, establishing its dominance over existing methods.

Estimation of Strawberry Canopy Volume in Unmanned Aerial Vehicle RGB Imagery Using an Object Detection-Based Convolutional Neural Network

Estimating canopy volumes of strawberry plants can be useful for predicting yields and establishing advanced management plans. Therefore, this study evaluated the spatial variability of strawberry canopy volumes using a ResNet50V2-based convolutional neural network (CNN) model trained with RGB images acquired through manual unmanned aerial vehicle (UAV) flights equipped with a digital color camera. A preprocessing method based on the You Only Look Once v8 Nano (YOLOv8n) object detection model was applied to correct image distortions influenced by fluctuating flight altitude under a manual maneuver. The CNN model was trained using actual canopy volumes measured using a cylindrical case and small expanded polystyrene (EPS) balls to account for internal plant spaces. Estimated canopy volumes using the CNN with flight altitude compensation closely matched the canopy volumes measured with EPS balls (nearly 1:1 relationship). The model achieved a slope, coefficient of determination (R2), and root mean squared error (RMSE) of 0.98, 0.98, and 74.3 cm3, respectively, corresponding to an 84% improvement over the conventional paraboloid shape approximation. In the application tests, the canopy volume map of the entire strawberry field was generated, highlighting the spatial variability of the plant’s canopy volumes, which is crucial for implementing site-specific management of strawberry crops.

3D distortion-free, reduced FOV diffusion-prepared gradient echo at 3 T.

To develop a 3D distortion-free reduced-FOV diffusion-prepared gradient-echo sequence and demonstrate its application in vivo for diffusion imaging of the spinal cord in healthy volunteers. A 3D multi-shot reduced-FOV diffusion-prepared gradient-echo acquisition is achieved using a slice-selective tip-down pulse in the phase-encoding direction in the diffusion preparation, combined with magnitude stabilizers, centric k-space encoding, and 2D phase navigators to correct for intershot phase errors. The accuracy of the ADC values obtained using the proposed approach was evaluated in a diffusion phantom and compared to the tabulated reference ADC values and to the ADC values obtained using a standard spin echo diffusion-weighted single-shot EPI sequence (DW-SS-EPI). Five healthy volunteers were scanned at 3 T using the proposed sequence, DW-SS-EPI, and a clinical diffusion-weighted multi-shot readout-segmented EPI sequence (RESOLVE) for cervical spinal cord imaging. Image quality, perceived SNR, and image distortion were assessed by two expert radiologists. ADC maps were calculated, and ADC values obtained with the proposed sequence were compared to those obtained using DW-SS-EPI and RESOLVE. Consistent ADC estimates were measured in the diffusion phantom with the proposed sequence and the conventional DW-SS-EPI sequence, and the ADC values were in close agreement with the reference values provided by the manufacturer of the phantom. In vivo, the proposed sequence demonstrated improved image quality, improved perceived SNR, and reduced perceived distortion compared to DW-SS-EPI, whereas all measures were comparable against RESOLVE. There were no significant differences in ADC values estimated in vivo for each of the sequences. 3D distortion-free diffusion-prepared imaging can be achieved using the proposed sequence.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

On the influence of artificially distorted images in firearm detection performance using deep learning

Detecting people carrying firearms in outdoor or indoor scenes usually identifies (or avoids) potentially dangerous situations. Nevertheless, the automatic detection of these weapons can be greatly affected by the scene conditions. Commonly, in real scenes these firearms can be seen from different perspectives. They also may have different real and apparent sizes. Moreover, the images containing these targets are usually cluttered, and firearms can appear as partially occluded. It is also common that the images can be affected by several types of distortions such as impulse noise, image darkening or blurring. All these perceived variabilities could significantly degrade the accuracy of firearm detection. Current deep detection networks offer good classification accuracy, with high efficiency and under constrained computational resources. However, the influence of practical conditions in which the objects are to be detected has not sufficiently been analyzed. Our article describes an experimental study on how a set of selected image distortions quantitatively degrade the detection performance on test images when the detection networks have only been trained with images that do not present the alterations. The analyzed test image distortions include impulse noise, blurring (or defocus), image darkening, image shrinking and occlusions. In order to quantify the impact of each individual distortion on the firearm detection problem, we have used a standard YOLOv5 network. Our experimental results have shown that the increased addition of impulse salt-and-pepper noise is by far the distortion that affects the most the performance of the detection network.

Assessment of trabecular bone score using updated TBSTT in anorexia nervosa-The AN-BO study.

Anorexia Nervosa (AN) is characterized by a distortion of body image, very low body weight, malnutrition and hormonal dysregulations, resulting in reduced bone mineral density (BMD) and impaired bone microarchitecture. The updated Trabecular Bone Score (TBS) algorithm accounts for soft tissue thickness (TBSTT) instead of BMI (TBSBMI). The aim of the study was to assess both TBS algorithms in adult AN patients compared to normal-weight controls(CTRL). This retrospective cross-sectional study investigated 34 adult female anorexia nervosa (AN) patients and 26 healthy normal-weighted age- and sex-matched controls (CTRL). Bone texture analysis was assessed by TBSTT and TBSBMI (TBS iNsight® V4.0 and V3.1), bone mineral density (BMD; lumbar spine LS, femoral neck, total hip) and body composition by DXA (GE Lunar iDXATM). Laboratory analyses included bone turnover markers (CTX; P1NP; sclerostin). Data analysis was performed using parametric (t-test) or non-parametric test (Mann-Whitney-U-Test) depending on normality, one-way ANCOVA and correlation analysis (Perason's or Spearman's). AN patients (BMI 14.7(1.6)) and CTRL (BMI 22.4(4.0)) were of comparable age (22.8(7.1) vs.25.0(4.0)years, p = 0.145). TBSTT(1.319±0.09 vs.1.502±0.07, p<0.001) and TBSBMI(1.317±0.10 vs.1.548±0.09, p<0.001) were significantly lower in AN patients compared to CTRL. Soft tissue thickness was lower in AN (p<0.001). Within the CTRL group, but not in AN, TBSTT and TBSBMI were significantly different (p<0.001). BMD was lower at all sites in AN patients (p<0.001 for all), being lowest at LS. Bone Mineral Content, Lean Body mass and Fat Mass were lower in AN (p<0.001). AN patients had lower P1NP (p = 0.05), but higher CTX (p = 0.001) and sclerostin (p = 0.003) levels. Adult AN patients have lower TBSTT and TBSBMI, reduced BMD and an uncoupling of bone turnover. In AN both TBS algorithms show similar reduced trabecular bone microarchitecture. The observed difference of TBSTT and TBSBMI in CTRL with normal body composition highlight the importance of the new algorithm.

3D-Aligner: advanced computational tool for correcting image distortion in expansion microscopy

Expansion Microscopy (ExM) is an innovative and cost-effective super-resolution microscopy technique that has become popular in cell biology research. It achieves super-resolution by physically expanding specimens. Since its introduction, ExM has undergone continuous methodological developments to enhance its resolution and labeling capabilities. However, ExM imaging often encounters sample drift during image acquisition due to the physical movement of the expanded hydrogel, posing a significant challenge for accurate image reconstruction. Despite many proposed experimental solutions to mitigate sample drift, a universal solution has yet to be established. In response to this challenge, we developed 3D-Aligner, an advanced and user-friendly image analysis tool designed to computationally correct drift in ExM images for precise three-dimensional image reconstruction and downstream quantification. We demonstrate that 3D-Aligner effectively determines and corrects drift in ExM images with different expansion rates and various fluorescently labeled biological targets, showcasing its capabilities and robustness in drift correction. Additionally, we validate the precision of 3D-Aligner by comparing drift values across different labeled targets and highlight the importance of drift correction in quantification of biological structures.

Experimental Assessment of Magnetic Resonance Imaging Distortion for Radiation Therapy Planning

MRI is widely used in radiation therapy planning, particularly in stereotactic radiosurgery for brain metastases. The article addresses the geometric distortion of MRI images, which can lead to errors in radiation therapy planning. A series of experiments with a simple phantom were conducted on two MRI scanners with 0.5 T and 1.5 T magnetic fields. Deviations in the positions of the phantom objects from their actual locations were observed. The detected deviations can reach up to 5 mm. A theoretical dependence of the magnetic field gradient on the distance to the center of homogeneity was calculated, which agrees well with the approximation of experimental data. An assessment was made of the dependence of the image area distortion of objects located at the center of the field homogeneity on their actual sizes. It was found that the area deviation can reach up to 20%.

Three hundred sixty‐degree viewable linear grooves 3D display

AbstractWe proposed a linear grooves 3D display that can display 360° viewable 3D images by creating linear grooves that correspond to the viewpoint positions. In the conventional method, arc‐shaped grooves are drawn on a flat substrate of transparent film. However, depending on the viewing position, the image may be distorted. In addition, when a flat substrate is bent into a curved shape, the image is more affected than one observed in a flat shape. Furthermore, the conventional method of arc 3D display method cannot display a 3D image that corresponds to the viewpoint position. In this paper, we propose design and fabrication methods that reduce the image distortion for both flat and curved shapes. A 3D image displayed by the proposed method can change depending on the viewpoint position, and a natural 3D image with motion parallax can be observed. In the experiment, our proposed method fabricated a 360° 3D display by calculating the linear grooves and presented natural 3D images with smooth motion parallax.

Employing Different Algorithms of Lightweight Convolutional Neural Network Models in Image Distortion Classification

The majority of applications use automatic image recognition technologies to carry out a range of tasks. Therefore, it is crucial to identify and classify image distortions to improve image quality. Despite efforts in this area, there are still many challenges in accurately and reliably classifying distorted images. In this paper, we offer a comprehensive analysis of models of both non-lightweight and lightweight deep convolutional neural networks (CNNs) for the classification of distorted images. Subsequently, an effective method is proposed to enhance the overall performance of distortion image classification. This method involves selecting features from the pretrained models’ capabilities and using a strong classifier. The experiments utilized the kadid10k dataset to assess the effectiveness of the results. The K-nearest neighbor (KNN) classifier showed better performance than the naïve classifier in terms of accuracy, precision, error rate, recall and F1 score. Additionally, SqueezeNet outperformed other deep CNN models, both lightweight and non-lightweight, across every evaluation metric. The experimental results demonstrate that combining SqueezeNet with KNN can effectively and accurately classify distorted images into the correct categories. The proposed SqueezeNet-KNN method achieved an accuracy rate of 89%. As detailed in the results section, the proposed method outperforms state-of-the-art methods in accuracy, precision, error, recall, and F1 score measures.

Comparison of Diffusion-weighted MRI using Singe-Shot Echo-planar Imaging (SS-EPI) and Split Acquisition of Fast Spin Echo Signal (SPLICE) Imaging, a non-EPI technique, in Tumors of the Head and Neck.

Diffusion-weighted imaging (DWI) using single-shot echo planar imaging (DW-EPI) is susceptible to distortions around air-filled cavities and dental fillings, typical for the head and neck area. Non-EPI, Split acquisition of fast spin echo signals for diffusion imaging (DWSPLICE) could reduce these distortions and enhance image quality, thereby potentially improving recurrence assessment in squamous cell carcinoma (SCC) of the head and neck region. This study evaluated whether DW-SPLICE is a viable alternative to DW-EPI through quantitative and qualitative analyses. The DW-SPLICE sequence was incorporated into the standard 3.0T head and neck MRI protocol with DW-EPI. Retrospective analysis was conducted on two subgroups: firstly benign or malignant lesions, and secondly post-treatment SCC recurrence. In both subgroups Image quality and distortion were scored by two independent radiologists, blinded for DW-technique, and evaluated using mixed-effect linear models. Lesion apparent diffusion coefficient (ADC) values were assessed with inter-class correlation (ICC) and Bland-Altman analyses. DWI's delineation geometric similarity to T1-weighted post-contrast (T1Wc) MRI was evaluated using the Dice Similarity Coefficient (DSC) before and after registration. Recurrence in post-treatment SCC scans was evaluated by the same two radiologists blinded for DW-technique. Recurrence detection rates were then compared between DW-SPLICE and DW-EPI using mixed logistic regression at six months and at one-year post-scan follow-up data. From August 2020 to January 2022, 55 benign or malignant lesion scans (55 patients) and 74 post-treatment SCC scans (66 patients) were analyzed. DW-SPLICE scored better on image quality and showed less overall distortion than DW-EPI (0.04<p<0.001). There was high ADC measurement reliability (ICC=0.93, p<0.001), though a proportional bias was also observed (β=0.11, p=0.03), indicating the bias increases as ADC values rise. DWSPLICE exhibited greater geometric similarity to T1Wc before registration (DSC 0.63 vs 0.47, p<0.001) and outperformed DW-EPI by more accurately identifying recurrences after one year (OR=0.96, p=0.05) but not after six months (OR=0.72, p=0.13). DW-SPLICE surpasses DW-EPI on image distortion and quality and improves diagnostic reliability for detecting recurrent or residual SCC on 3T MRI of the HN. Consistent use of one method for follow-up is advised, as ADC values are not completely interchangeable. Integrating DW-SPLICE can significantly improve tumor assessments in clinical practice. ANTs = Advanced Normalization Tools; DSC = Dice Similarity Coefficient; DW-EPI = Diffusion-weighted single-shot echo planar imaging; DW-MS-EPI = Diffusion-weighted multi-shot echo planar imaging; DW-SPLICE = Diffusion-weighted split acquisition of fast spin echo signals for diffusion imaging; DW-TSE = Diffusion-weighted Turbo Spin Echo; ICC = Intraclass Correlation Coefficient; ROC = Receiver Operating Characteristic; SCC = Squamous cell carcinoma; T1WIc = T1 weighted imaging with gadolinium contrast.

Critical Impact of Working Distance on OCT Imaging: Correction of Optical Distortion and Its Effects on Measuring Retinal Curvature.

To assess the impact of working distance (WD) on optical distortion in optical coherence tomography (OCT) imaging and to evaluate the effectiveness of optical distortion correction in achieving consistent retinal Bruch's membrane (BM) layer curvature, regardless of variations in WD. Ten subjects underwent OCT imaging with four serial macular volume scans, each employing distinct WD settings adjusted by balancing the sample and reference arm of the OCT interferometer (eye length settings changed). Either of two types of 30° standard objectives (SOs) was used. A ray tracing model was used to correct optical distortion, and BM layer curvature (represented as the second derivative of the curve) was measured. Linear mixed effects (LME) modeling was used to analyze factors associated with BM layer curvature, both before and after distortion correction. WD exhibited significant associations with axial length (β = -1.35, P < 0.001), SO type (P < 0.001), and eye length settings (P < 0.001). After optical distortion correction, the mean ± SD BM layer curvature significantly increased from 16.80 ± 10.08µm-1 to 49.31 ± 7.50µm-1 (P < 0.001). The LME model showed a significant positive association between BM layer curvature and WD (β = 1.94, P < 0.001). After distortion correction, the percentage change in BM layer curvature due to a 1-mm WD alteration decreased from 9.75% to 0.25%. Correcting optical distortion in OCT imaging significantly mitigates the influence of WD on BM layer curvature, enabling a more accurate analysis of posterior eye morphology, especially when variations in WD are unavoidable.

No-reference image quality assessment based on global awareness.

In the field of computer vision, the application of hand-crafted as well as computer-learning-based methods in the field of image quality assessment has yielded remarkable results. However, in the field of no-reference image distortion, it is still challenging to accurately perceive and determine the quality of an image. To address the difficulties of Image Quality Assessment (IQA) in the field of authentic distorted images, we consider the use of the Swin Transformer (ST) to extract features. To enable the model to focus on both spatial and channel information of features, we design a plug-and-play Global Self-Attention Block (GSAB). At the same time, we introduce a Transformer block in the model to enhance the model's ability to capture long-range dependencies. Finally, we derive the prediction of image quality scores through a Dual-Branching structure. Our method is experimented on four synthetic datasets as well as two authentic datasets, and the results of the experiments are weighted according to the size of the datasets, and the results show that our method outperforms all the current methods and works well in the Generalization ability test, which proves that our method has a good generalization ability. The code will be posted subsequently at https://github.com/yanggege-new/NR-IQA-based-on-global-awareness.

Effects of Parallax and Distortion in Total Ankle Arthroplasty.

Surgeons rely on intraoperative fluoroscopy to assist in placement of implant components during total ankle arthroplasty (TAA). Parallax alters the direction of an object when viewed from two different points, resulting in image distortion. The purpose of this study was to evaluate parallax/distortion in intraoperative fluoroscopic images during TAA. A retrospective review of all TAAs performed by two surgeons (R.W.M. and B.S.) from August 2019 to April 2023 were reviewed. Intraoperative fluoroscopic anteroposterior (AP) ankle views were evaluated for any obvious parallax image distortion. Cases with obvious parallax distortion were included for angular evaluation of AP intraoperative fluoroscopic and first postoperative plain films. The tibia was marked at 2-centimeter intervals to create zones from the proximal stem of the implant. The anatomical axis of the tibia (AAT) was drawn at the mid-diaphysis. The anatomic lateral distal tibial angle (aLDTA) and anatomic axis deviation (AAD) were measured for each zone. A total of 22 TAAs were performed during the study period. Four cases were excluded due to inadequate imaging, leaving a total of 18 TAAs for review. We found 6 of 18 (33.3%) cases had obvious parallax distortion. We found the average aLDTA was 90.9° (84°-101°). At the most proximal tibial zone, the average aLDTA was 94° (91°-101°). We found the average AAD was 4.7 (0.5-17.2) mm. The AAD ranged from 0.5 to 17.2 mm lateral to 0.8 to 8.2 mm medial. Postoperative plain film radiographs displayed a normal aLDTA and an AAT centered within the ankle joint. Parallax can distort the appearance of the tibia on fluoroscopic images. Deviation from the normal aLDTA and anatomical axis should be anticipated. Surgeons should be aware of the potential impact of parallax and ways to mitigate these effects.

Comparison of single shot and multishot diffusion-weighted imaging in 5-T magnetic resonance imaging for brain disease diagnosis.

Diffusion-weighted imaging (DWI) with single-shot echo-planar imaging (ssEPI) is a valuable tool for detecting acute brain lesions but does suffer from image distortions. Multishot echo-planar imaging (msEPI) is a technique for reducing such distortions. This study aimed to compare the image quality and diagnostic efficacy of ssEPI- and msEPI-DWI at 5.0 T for brain disease detection. This study retrospectively reviewed images of 107 consecutive patients with suspected brain diseases who underwent ssEPI- and msEPI-DWI at 5.0 T at the First Affiliated Hospital of University of Science and Technology of China from August 2023 to September 2023. Two radiologists independently graded image quality and measured the image distortion. Signal-to-noise ratio, contrast-to-noise ratio, and apparent diffusion coefficient (ADC) were calculated and compared between ssEPI- and msEPI-DWI. Image quality scores were compared using the Wilcoxon test and other continuous variables by the paired t-test. The diagnostic accuracy of ADC values in distinguishing lesions from normal-appearing tissues was measured with the area under the curve (AUC). Image quality evaluation and distortion analysis revealed that msEPI-DWI significantly outperformed ssEPI-DWI (two-sided P<0.001). No significant difference was observed in signal-to-noise ratio, contrast-to-noise ratio, or ADC values between msEPI- and ssEPI-DWI (two-sided P≥0.601). The ADC values of msEPI- and ssEPI-DWI showed strong correlations for both lesions (r=0.97) and contralateral normal tissues (r=0.91) (two-sided P<0.001). Compared to those of the contralateral white matter, ADC values of low-grade gliomas (LGGs) were significantly higher [ssEPI-DWI: 1,119.9±273.1 vs. 805.1±73.9; msEPI-DWI: 1,196.2±355.6 vs. 757.3±98.0 (unit: ×10-6 mm2/s)], while the ADC values of acute cerebral infarction (ACI) lesions were significantly lower [ssEPI-DWI: 603.9±273.2 vs. 888.9±212.0; msEPI-DWI: 538.0±281.2 vs. 905.0±188.9 (unit: ×10-6 mm2/s)] (two-sided P≤0.003). The AUCs for detecting LGGs were excellent for both ssEPI-DWI [AUC =0.934; 95% confidence interval (CI): 0.84-1.00] and msEPI-DWI (AUC =0.944; 95% CI: 0.86-1.00) (two-sided P<0.001; two-sided DeLong test: P=0.833). As compared to ssEPI-DWI, msEPI-DWI, when performed at 5.0 T, demonstrated superior image quality and less anatomical distortion in a wide spectrum of brain diseases and showed promising diagnostic performance for LGGs and ACI. In the future, msEPI-DWI at 5.0 T could become clinically routine in the diagnosis and grading of brain disorders.

Determining The Lens Aberations on Positive Lenses Through Science Process Skill

Abstract This research aims to determine aberrations or image distortions in a positive lens using an optics experiment kit for image distortions. The experimental tool consists of a diaphragm, a positive lens, a light source, lens path, an image capture screen, and a flint glass used to reduce lens distortions. The main tool is made of acrylic, which is durable and easy to use. The lens used is a strong positive lens (+100). The diaphragms used vary, including a slit diaphragm with five slits and a four-ring diaphragm. These diaphragms are used to produce image shapes and observe the distortions that occur. The work process refers to the skills of scientific processes such as observation, classification, measurement, prediction, communication, and conclusion, which are implemented in the work instructions for creating aberrations or image distortions by placing the positive lens parallel to the light source and the image capture screen. Then, the distortions that occur are observed, and a flint glass is placed in front of the light source to reduce the distortions that are formed. By observing the resulting image, it can be seen that the distortions that occur on the positive lens and how the flint glass works to reduce these aberrations. This is in line with the theory that aberrations on a positive lens occur due to imperfect light bias, either because of the different refractive index biases from different media in the lens, or because of the lens shape not being ideal. Flint glass is one of the methods used to reduce these aberrations.

An Image Distortion Correction Method Based on Distortion Centre and Parameter Estimation

Abstract We propose a radial distortion correction method based on straight-line feature calibration to address the challenge of image radial distortion when measuring mechanical motion parameters using computer vision technology. This method first independently estimates the distortion center, and then transforms the distortion parameter estimation into an arc fitting problem. The least squares optimization algorithm is applied to quickly and accurately estimate the arc parameter of distorted lines, ultimately achieving precise correction of distortion in wide-angle camera images. This method can reduce lens distortion by 89% to 92%, laying a solid foundation for the application of computer vision technology in the field of mechanical motion parameter measurement.

Retinal fundus image super-resolution based on generative adversarial network guided with vascular structure prior

Many ophthalmic and systemic diseases can be screened by analyzing retinal fundus images. The clarity and resolution of retinal fundus images directly determine the effectiveness of clinical diagnosis. Deep learning methods based on generative adversarial networks are used in various research fields due to their powerful generative capabilities, especially image super-resolution. Although Real-ESRGAN is a recently proposed method that excels in processing real-world degraded images, it suffers from structural distortions when super-resolving retinal fundus images are rich in structural information. To address this shortcoming, we first process the input image using a pre-trained U-Net model to obtain a structural segmentation map of the retinal vessels and use the segmentation map as the structural prior. The spatial feature transform layer is then used to better integrate the structural prior into the generation process of the generator. In addition, we introduce channel and spatial attention modules into the skip connections of the discriminator to emphasize meaningful features and accordingly enhance the discriminative power of the discriminator. Based on the original loss functions, we introduce the L1 loss function to measure the pixel-level differences between the segmentation maps of retinal vascular structures in the high-resolution images and the super-resolution images to further constrain the super-resolution images. Simulation results on retinal image datasets show that our improved algorithm results have a better visual performance by suppressing structural distortions in the super-resolution images.

Constructive and destructive effects of mediatization of family memory

The paper analyzes the peculiarities of family memory transformation under the influence of digitalization. In the modern world, new media begin to exert a determining influence on the formation of the image of reality, leading to the emergence of a special media culture that transforms familiar social phenomena. To reveal the specifics of the phenomenon of family memory we use the concept of midi-memory, which describes the mechanisms of emergence, storage and translation of collective representations of the past in the conditions of mediatization and digitalization. In such conditions, the boundary between cultural and communicative memory is gradually erased, and the number of subjects involved in the construction of representations of the past increases significantly. As a result, the mechanism of constructing images of the past is changing. These processes lead to the need to analyze the destructive and constructive effects of the mediatization of collective and, in particular, family memory.The main methodological principle of the study is the analysis of various forms of collective memory that appear in modern reality under the influence of digitalization. In modern realities, which are gradually blurring the line between cultural and communicative memory, collective representations of the past are being transferred into the format of media memory. Mediatization of family memory can contribute to the formation of national identity through the inclusion of significant national events in the images of the past present in family memory. A significant role in these processes is played by significant symbols of the Soviet period, broadcast by modern media practice. This increases the risk of purposeful distortion of images of the past. In the end, the media blur the boundaries of family memory, replacing images of the past with images constructed by media space.