Scale Of Image Research Articles

Image reconstruction with the use of fresnel holograms: relationship between the longitudinal and transverse scales of images

Image reconstruction with the use of fresnel holograms: relationship between the longitudinal and transverse scales of images

Assessing the utility of computer vision for age determination of Gulf Menhaden

AbstractObjectiveIn this work, we assess the potential of computer vision techniques for age estimation of Gulf Menhaden Brevoortia patronus scales. Scales are the primary structure used for the age determination of Gulf Menhaden, and the ageing process can be labor intensive. Gulf Menhaden is the second‐largest fishery by weight in the United States, with average annual landings from 2018 to 2022 of 449,540 metric tons, and is assessed with age‐structured models that require information about the age structure of the catch and the stock.MethodsWe used convolutional neural networks and deep neural networks to classify the age from images of Gulf Menhaden scales from three different sets of images of scales. The first set of data consists of images of scales from fish at ages 0 and 1 year. The second set of data consists of images of scales from fish at ages 0–4 years. The last set of data consists of images of scales from fish of ages 0, 1, and 2 years and includes only images of scales for which there is agreement by readers of age estimates derived from analyzing sagittal otoliths and scales from the same individual.ResultThe classification of ages was best when using a convolutional neural network model on the first data set. The poorest classification was for the model using deep neural networks with the second data set.ConclusionAlthough we show that computer vision has promise for age determination from fish scale samples, our results indicate that considerable work must be done for wide adoption of the approach. With the continuous enhancements of computer vision models, improvements in the quality of scale images, and the accumulation of larger sets of scale images that can be used to train machine learning models, we believe that using computer vision can serve to reduce processing time and increase the accuracy of age estimates.

Sensitive Information Detection Based on Deep Learning Models

Currently, a large number of pornographic images on the Internet severely affect the growth of adolescents. In order to create a healthy and benign online environment, it is necessary to recognize and detect these sensitive images. Current techniques for detecting pornographic content are still in an immature stage, with the key issue being low detection accuracy. To address this problem, this paper proposes a method for detecting pornographic content based on an improved YOLOv8 model. Firstly, InceptionNeXt is introduced into the backbone network to enhance the model’s adaptability to images of different scales and complexities by optimizing feature extraction through parallel branches and deep convolution. Simultaneously, the SPPF module is simplified into the SimCSPSPPF module, which further enhances the effectiveness and diversity of features through improved spatial pyramid pooling and cross-layer feature fusion. Secondly, switchable dilated convolutions are incorporated to improve the adaptability of the C2f enhancement model and enhance the model’s detection capability. Finally, SEAattention is introduced to enhance the model’s ability to capture spatial details. The experiments demonstrate that the model achieves an mAP@0.5 of 79.7% on our self-made sensitive image dataset, which is a significant improvement of 5.9% compared to the previous YOLOv8n network. The proposed method excels in handling complex backgrounds, targets of varying scales, and resource-constrained scenarios, while simultaneously improving the model’s computational efficiency without compromising detection accuracy, making it more advantageous for practical applications.

Analysing the capacity of multispectral indices to map the spatial distribution of potential post-fire soil losses based on soil burn severity

The area burned in Spain exceeded historical records in 2022, when exceptionally warm conditions influenced wildfire events. The predicted intensification of wildfire regimes includes an increase in frequency, severity, and size. Therefore, a study of the wildfires that occurred in 2022 is necessary to understand their behaviour and possible environmental impacts. The objective of this study is to analyse the applicability of using spectral indices and Geographic Information System (GIS) approaches to map the spatial distribution and estimate potential soil losses using Sentinel-2 imagery and fire severity field data. Soil losses were estimated using an empirical model based on soil burn severity data collected in the field after wildfire. The relationship between the Normalized Difference Infrared Index (NDII), Difference Normalized Wildfire Ash Index (dNWAI), and the Blue Normalized Difference Vegetation Index (BNDVI) with the estimated soil losses was then evaluated. In addition, the influence of different time scales of the satellite images was analysed. The first period considered (Date I) ranges from 8 to 20 days after the beginning of the wildfire, which coincides with the field data collection. The second period considered (Date II) ranges from 28 to 35 days after the start of the wildfire. The results obtained showed a significant dependence relationship between the BNDVI index (using satellite images of Date I) and the estimated soil losses (R2 = 0.756), while the results of the NDII (R2 = 0.31) and dNWAI (R2 = 0.061), showed no spatial relationship with the estimated soil losses. Three of the largest wildfires in 2022 in Spain were analysed, and the results showed strong correlations of BNDVI index for Folgoso do Courel (R2 = 0.808), for Carballeda de Valedorras (R2 = 0.906), and for Sierra de la Culebra (R2 = 0.939). In addition, these results allowed the mapping and quantification of potential soil losses in areas where fire severity was high, totalling ∼2,50,000 Mg ha−1 in Folgoso do Courel, ∼3,70,000 Mg ha−1 in Carballeda de Valdeorras, and ∼4,70,000 Mg ha−1 in Sierra de la Culebra. Moreover, BNDVI values for estimating soil loss vary by vegetation type, and there is a positive correlation between severity classes and the BNDVI index. This approach can inform post-fire land management decisions in future wildfires and could be applied to other regions.

An Effective and Lightweight Full-Scale Target Detection Network for UAV Images Based on Deformable Convolutions and Multi-Scale Contextual Feature Optimization

Currently, target detection on unmanned aerial vehicle (UAV) images is a research hotspot. Due to the significant scale variability of targets and the interference of complex backgrounds, current target detection models face challenges when applied to UAV images. To address these issues, we designed an effective and lightweight full-scale target detection network, FSTD-Net. The design of FSTD-Net is based on three principal aspects. Firstly, to optimize the extracted target features at different scales while minimizing background noise and sparse feature representations, a multi-scale contextual information extraction module (MSCIEM) is developed. The multi-scale information extraction module (MSIEM) in MSCIEM can better capture multi-scale features, and the contextual information extraction module (CIEM) in MSCIEM is designed to capture long-range contextual information. Secondly, to better adapt to various target shapes at different scales in UAV images, we propose the feature extraction module fitting different shapes (FEMFDS), based on deformable convolutions. Finally, considering low-level features contain rich details, a low-level feature enhancement branch (LLFEB) is designed. The experiments demonstrate that, compared to the second-best model, the proposed FSTD-Net achieves improvements of 3.8%, 2.4%, and 2.0% in AP50, AP, and AP75 on the VisDrone2019, respectively. Additionally, FSTD-Net achieves enhancements of 3.4%, 1.7%, and 1% on the UAVDT dataset. Our proposed FSTD-Net has better detection performance compared to state-of-the-art detection models. The experimental results indicate the effectiveness of the FSTD-Net for target detection in UAV images.

A ruler detection method for auto-adjusting scales of shoeprint images

Digital shoeprint comparison often requires the calibration of the image resolution so that features, such as patterns in shoeprints, can be compared on the same scale. To enable scaling, a shoeprint photograph can be taken with a forensic ruler in the same frame to obtain the pixel distance between two nearby graduations. However, manually measuring the number of pixels is a time-consuming process. Additionally, the measurement process might not be conducted accurately when the image is noisy or there is distortion in the ruler. In this study, we present an automated ruler detection method for adjusting the image scale. We show that this method can accurately estimate the image scale with a mean absolute percentage error of 3%. We also conducted automated shoeprint retrieval experiments on scale-unadjusted shoeprint images to show how the automated image scaling might be used in a common forensic process. Our results from these experiments show an increase in the retrieval performance from 0.735 to 0.929 at S1 by employing this approach to adjust the shoeprint image scales.

Accelerating breast MRI acquisition with generative AI models.

To investigate the use of the score-based diffusion model to accelerate breast MRI reconstruction. We trained a score-based model on 9549 MRI examinations of the female breast and employed it to reconstruct undersampled MRI images with undersampling factors of 2, 5, and 20. Images were evaluated by two experienced radiologists who rated the images based on their overall quality and diagnostic value on an independent test set of 100 additional MRI examinations. The score-based model produces MRI images of high quality and diagnostic value. Both T1- and T2-weighted MRI images could be reconstructed to a high degree of accuracy. Two radiologists rated the images as almost indistinguishable from the original images (rating 4 or 5 on a scale of 5) in 100% (radiologist 1) and 99% (radiologist 2) of cases when the acceleration factor was 2. This fraction dropped to 88% and 70% for an acceleration factor of 5 and to 5% and 21% with an extreme acceleration factor of 20. Score-based models can reconstruct MRI images at high fidelity, even at comparatively high acceleration factors, but further work on a larger scale of images is needed to ensure that diagnostic quality holds. The number of MRI examinations of the breast is expected to rise with MRI screening recommended for women with dense breasts. Accelerated image acquisition methods can help in making this examination more accessible. Accelerating breast MRI reconstruction remains a significant challenge in clinical settings. Score-based diffusion models can achieve near-perfect reconstruction for moderate undersampling factors. Faster breast MRI scans with maintained image quality could revolutionize clinic workflows and patient experience.

Industry Image Classification Based on Stochastic Configuration Networks and Multi-Scale Feature Analysis.

For industry image data, this paper proposes an image classification method based on stochastic configuration networks and multi-scale feature extraction. The multi-scale features are extracted from images of different scales using deep 2DSCN, and the hidden features of multiple layers are also connected together to obtain more informational features. The integrated features are fed into SCNs to learn a classifier which improves the recognition rate for different categories. In the experiments, a handwritten digit database and an industry hot-rolled steel strip database are used, and the comparison results demonstrate the proposed method can effectively improve the classification accuracy.

Evaluation of seedless wavelet-based optical flow velocimetry for schlieren images

In harsh flow environments, traditional particle-based velocimetry methods face challenges. This study explores the use of seedless schlieren images for velocimetry through a novel algorithm, namely, wavelet-based optical flow velocimetry (wOFV). Various data term constraints for wOFV were examined. It is found that the data term derived from the integrated continuity equation (ICE) outperformed the conventional displaced frame difference constraint and the schlieren-tailored constraints (SE and SSE). Evaluation based on the root mean square error (RMSE) and turbulence energy spectrum (TES) reveals that the choice of wavelet becomes insignificant for the optimal estimated velocity field when the wavelet support length is sufficiently long. In addition, the implementation of a proper truncation in wOFV shows little dependence of the RMSE on the weighting coefficient, therefore alleviating the uncertainty associated with selecting an appropriate weighting coefficient. It is found that the retrieved flow field from schlieren images approximates a down-sampled result based on available structural scales in images. Considering the prevalence of under-resolved velocity field in practical applications, schlieren-based wOFV offers a reasonable alternative to particle-based velocimetry, particularly in harsh flow environments.

A scheme of hiding large-size image into small-size image based on FCdDNet.

The hiding capacity of the current information hiding field has reached a relatively high level, which can hide two color images into one color image. In order to explore a larger hidden capacity, an information hiding scheme based on an improved FCdDNet is proposed, which can hide large-size color images into small-size color images. An improved FCdDNet network is used as the main structure shared by the hidden network and the extraction network. These two networks promote and improve each other during the confrontation training process and are used in pairs. It can be seen that the proposed scheme achieves a larger information hiding capacity, and the hidden information is four times larger than the scale of the carrier image. At the same time, the visual effect after hiding is guaranteed, and the image extracted from the hidden image also has a high degree of restoration. The scheme can be applied to image authentication, secret image transmission, and other fields.

Reconstruction of images using Fresnel holograms: the relationship between the longitudinal and transverse scales of images

The article is devoted to the study of changes in the relationship between the transverse and longitudinal scales of images. The possibility of changing the classical quadratic relationship between the longitudinal and transverse scales of images is considered, while when using traditional imaging methods in classical optics this is impossible due to the homogeneity and isotropy of space. An analysis of image scale transformations for optical schemes of Fresnel holography using spatially incoherent radiation has been carried out. It is shown that recording a Fresnel hologram allows one to change the ratio between the transverse and longitudinal scales of the reconstructed image within a fairly wide range. The results are relevant for use in those areas of science and technology that require the creation of images that reflect real-world objects as accurately as possible without distortion, for example, in the field of medical imaging.

Deep Learning Based Cystoscopy Image Enhancement.

Background: Endoscopy image enhancement technology provides doctors with clearer and more detailed images for observation and diagnosis, allowing doctors to assess lesions more accurately. Unlike most other endoscopy images, cystoscopy images face more complex and diverse image degradation because of their underwater imaging characteristics. Among the various causes of image degradation, the blood haze resulting from bladder mucosal bleeding make the background blurry and unclear, severely affecting diagnostic efficiency, even leading to misjudgment. Materials and Methods: We propose a deep learning-based approach to mitigate the impact of blood haze on cystoscopy images. The approach consists of two parts as follows: a blood haze removal network and a contrast enhancement algorithm. First, we adopt Feature Fusion Attention Network (FFA-Net) and transfer learning in the field of deep learning to remove blood haze from cystoscopy images and introduce perceptual loss to constrain the network for better visual results. Second, we enhance the image contrast by remapping the gray scale of the blood haze-free image and performing weighted fusion of the processed image and the original image. Results: In the blood haze removal stage, the algorithm proposed in this article achieves an average peak signal-to-noise ratio of 29.44 decibels, which is 15% higher than state-of-the-art traditional methods. The average structural similarity and perceptual image patch similarity reach 0.9269 and 0.1146, respectively, both superior to state-of-the-art traditional methods. Besides, our method is the best in keeping color balance after removing the blood haze. In the image enhancement stage, our algorithm enhances the contrast of vessels and tissues while preserving the original colors, expanding the dynamic range of the image. Conclusion: The deep learning-based cystoscopy image enhancement method is significantly better than other traditional methods in both qualitative and quantitative evaluation. The application of artificial intelligence will provide clearer, higher contrast cystoscopy images for medical diagnosis.

Semi-supervised skin cancer diagnosis based on self-feedback threshold focal learning

Worldwide, skin cancer prevalence necessitates accurate diagnosis to alleviate public health burdens. Although the application of artificial intelligence in image analysis and pattern recognition has improved the accuracy and efficiency of early skin cancer diagnosis, existing supervised learning methods are limited due to their reliance on a large amount of labeled data. To overcome the limitations of data labeling and enhance the performance of diagnostic models, this study proposes a semi-supervised skin cancer diagnostic model based on Self-feedback Threshold Focal Learning (STFL), capable of utilizing partial labeled and a large scale of unlabeled medical images for training models in unseen scenarios. The proposed model dynamically adjusts the selection threshold of unlabeled samples during training, effectively filtering reliable unlabeled samples and using focal learning to mitigate the impact of class imbalance in further training. The study is experimentally validated on the HAM10000 dataset, which includes images of various types of skin lesions, with experiments conducted across different scales of labeled samples. With just 500 annotated samples, the model demonstrates robust performance (0.77 accuracy, 0.6408 Kappa, 0.77 recall, 0.7426 precision, and 0.7462 F1-score), showcasing its efficiency with limited labeled data. Further, comprehensive testing validates the semi-supervised model’s significant advancements in diagnostic accuracy and efficiency, underscoring the value of integrating unlabeled data. This model offers a new perspective on medical image processing and contributes robust scientific support for the early diagnosis and treatment of skin cancer.

Video frame interpolation via spatial multi‐scale modelling

AbstractVideo frame interpolation (VFI) is a technique that synthesises intermediate frames between adjacent original video frames to enhance the temporal super‐resolution of the video. However, existing methods usually rely on heavy model architectures with a large number of parameters. The authors introduce an efficient VFI network based on multiple lightweight convolutional units and a Local three‐scale encoding (LTSE) structure. In particular, the authors introduce a LTSE structure with two‐level attention cascades. This design is tailored to enhance the efficient capture of details and contextual information across diverse scales in images. Secondly, the authors introduce recurrent convolutional layers (RCL) and residual operations, designing the recurrent residual convolutional unit to optimise the LTSE structure. Additionally, a lightweight convolutional unit named separable recurrent residual convolutional unit is introduced to reduce the model parameters. Finally, the authors obtain the three‐scale decoding features from the decoder and warp them for a set of three‐scale pre‐warped maps. The authors fuse them into the synthesis network to generate high‐quality interpolated frames. The experimental results indicate that the proposed approach achieves superior performance with fewer model parameters.

Application of Multimedia Data Feature Extraction Technology in Folk Art Creation

In the intelligent background, in order to carry out the folk art creation more conveniently, this article integrates the new technology of multimedia data features into the folk art creation to achieve the sustainable development of folk art. Specifically, based on the method of wavelet transform, this article decomposes folk art images into images of different scales and different resolutions to obtain clearer works, which is beneficial to the creation and dissemination of folk art. With the peak signal to noise ratio (PSNR) as the evaluation criterion, five classical folk art images are used to test the effect of image enhancement. The experimental results show that the PSNR of the folk art works after data feature extraction is higher than 30, which meets the requirements of data feature extraction. It further shows that multimedia digital feature technology has a good application effect on folk art works, which is conducive to the inheritance and creation of folk art.

Abstract 892: Pan-cancer tumor microenvironment profiling with multiplexed immunofluorescence microscopy and self-supervised learning

Abstract Multiplexed immunofluorescence (mIF) microscopy reveals the spatial architecture of cancer tissue and its microenvironment that is not being fully explored with existing analysis methods. Most analysis approaches for mIF microscopy data focus on single-cell or region classification and rely on supervised machine learning. In this work, we present a self-supervised spatial profiling method for mIF images that takes local and global associations into account, and apply this method to profile cancer-associated fibroblasts (CAFs) in a pan-cancer dataset. We studied a 2-stage self-supervised training scheme to learn the representations of mIF tissue microarray (TMA) images in local and global scales (cellular and long-range associations). During the 1st self-supervised training stage, a Vision Transformer learns the local-scale representations by using small patches from TMA images. Then, the local representations are used as input to the 2nd stage where a similar self-supervised learning strategy is used to learn global patterns in the TMA images. We applied the method to profile multiple cohorts from three different solid tumors: prostate, renal, and lung cancer. In total, these cohorts include more than 5,000 TMA cores from over 1,750 patients extracted from the tumor center, tumor edge, and adjacent benign areas. The samples were stained with a CAF panel including FAP, aSMA, PDGFRB, pSTAT3/PDGFRA, nuclear and epithelial markers, and imaged with cyclic mIF microscopy. Samples were studied at the patch-level and TMA core-level. Small patches enable further analysis of associations in the local environment, whereas the core-level enables associations with patient clinical information. Clustering of patch-level (1st stage) and core-level (2nd stage) representations showed independently that self-supervised learning is capable of learning the representations of the mIF images. We were able to identify regions and cases with high pTNM staging from the prostate cancer samples and similar histological subtypes from renal cancer samples. We further validated the clustering using k-NN classification that showed high classification accuracy in all cohorts. Moreover, we developed a stopping criteria for the final model selection that balances between the similarities of samples and patches inside of samples to prevent overfitting. Our study shows that self-supervised learning enables unbiased discoveries from large-scale mIF microscopy imaging datasets. The developed method uncovers associations between imaging data and clinical information and highlights directly the patterns that are most meaningful for these associations. Citation Format: Gantugs Atarsaikhan, Isabel Mogollon, Katja Välimäki, Tuomas Mirtti, Teijo Pellinen, Lassi Paavolainen. Pan-cancer tumor microenvironment profiling with multiplexed immunofluorescence microscopy and self-supervised learning [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 892.

ADF-Net: An Attention-Guided Dual-Branch Fusion Network for Building Change Detection near the Shanghai Metro Line Using Sequences of TerraSAR-X Images

Building change detection (BCD) plays a vital role in city planning and development, ensuring the timely detection of urban changes near metro lines. Synthetic Aperture Radar (SAR) has the advantage of providing continuous image time series with all-weather and all-time capabilities for earth observation compared with optical remote sensors. Deep learning algorithms have extensively been applied for BCD to realize the automatic detection of building changes. However, existing deep learning-based BCD methods with SAR images suffer limited accuracy due to the speckle noise effect and insufficient feature extraction. In this paper, an attention-guided dual-branch fusion network (ADF-Net) is proposed for urban BCD to address this limitation. Specifically, high-resolution SAR images collected by TerraSAR-X have been utilized to detect building changes near metro line 8 in Shanghai with the ADF-Net model. In particular, a dual-branch structure is employed in ADF-Net to extract heterogeneous features from radiometrically calibrated TerraSAR-X images and log ratio images (i.e., difference images (DIs) in dB scale). In addition, the attention-guided cross-layer addition (ACLA) blocks are used to precisely locate the features of changed areas with the transformer-based attention mechanism, and the global attention mechanism with the residual unit (GAM-RU) blocks is introduced to enhance the representation learning capabilities and solve the problems of gradient fading. The effectiveness of ADF-Net is verified using evaluation metrics. The results demonstrate that ADF-Net generates better building change maps than other methods, including U-Net, FC-EF, SNUNet-CD, A2Net, DMINet, USFFCNet, EATDer, and DRPNet. As a result, some building area changes near metro line 8 in Shanghai have been accurately detected by ADF-Net. Furthermore, the prediction results are consistent with the changes derived from high-resolution optical remote sensing images.

A deep integrative approach for diabetic retinopathy classification with synergistic channel-spatial and self-attention mechanism

Diabetic retinopathy (DR) is a prevalent eye disease that poses a significant risk of vision loss in individuals with diabetes. Accurate classification of DR is crucial for timely intervention and effective treatment. However, the classification task is challenging due to imbalanced datasets, small lesions, and inter-class similarity. This study proposes a novel deep integrative approach for DR classification, leveraging the strengths of residual blocks, channel-spatial attention mechanism (CSAM), and non-local blocks (NLB). The proposed architecture consists of a sequence of residual blocks followed by CSAM modules, which enhance feature discriminability at different scales in DR images. Additionally, the outputs from CSAM blocks are fed into an NLB to capture long-range dependencies, allowing the model to consider global context information. The proposed architecture primarily utilizes the strength of global features extracted from non-local blocks and fuses them to enrich the information representation. Experimental results demonstrate that the proposed method effectively addresses challenges in DR classification and shows superior performance in terms of computational time and improved accuracy compared to existing methods.

Prevalence of poor body image and its correlation with self-esteem and depression in middle-aged women

Objective This study aimed to determine the prevalence and predictors of poor body image and its influence on low self-esteem and depression in middle-aged women. Methods The study was a cross-sectional survey of 372 women using a predesigned online questionnaire. Main outcome measures were mean scores on the nine-point body image scale (BIS) and prevalence of poor body image (BIS ≥ 10). Significant sociodemographic, physical and mental health predictors of BIS ≥ 10 were established using logistic regression analysis. Correlation of body image with self-esteem, assessed by Rosenberg’s self-esteem scale, and depression, assessed by Beck’s Depression Inventory, was established using Pearson’s correlation coefficient and the odds ratios were determined using logistic regression analysis. Results The mean scores obtained for body image were 5.52 (standard deviation 4.82) and the prevalence of BIS ≥ 10 was 17.4%. Significant predictors of poor body image were education status (0.59, 95% confidence interval [CI] = 0.39–0.89), body mass index (2.26, 95% CI = 1.12–4.55), social support structure (0.35, 95% CI = 0.16–0.79), diagnosis of mental illness (3.74, 95% CI = 1.31–10.66) and perception of menopause (positive vs. negative: 0.20, 95% CI = 0.07–0.55). Body image scores correlated with self-esteem (r = −0.46; 95% CI = −0.54 to −0.38) and depression (r = 0.59; 95% CI = 0.52–0.65). BIS ≥ 10 significantly increased the odds of low self-esteem (5.71; 95% CI = 2.66–12.26) and depression (11.25; 95% CI = 4.9–27.7). Conclusions The prevalence of poor body image in middle-aged women was 17.4%. This strongly correlated with low self-esteem and depression. A lower education status, higher body mass index, a negative perception of menopause, poor social support structure and a history of mental health diagnosis increased the odds of poor body image prevalence in middle-aged women.

Image deblurring by multi-scale modified U-Net using dilated convolution.

In modern urban traffic systems, intersection monitoring systems are used to monitor traffic flows and track vehicles by recognizing license plates. However, intersection monitors often produce motion-blurred images because of the rapid movement of cars. If a deep learning network is used for image deblurring, the blurring of the image can be eliminated first, and then the complete vehicle information can be obtained to improve the recognition rate. To restore a dynamic blurred image to a sharp image, this paper proposes a multi-scale modified U-Net image deblurring network using dilated convolution and employs a variable scaling iterative strategy to make the scheme more adaptable to actual blurred images. Multi-scale architecture uses scale changes to learn the characteristics of different scales of images, and the use of dilated convolution can improve the advantages of the receptive field and obtain more information from features without increasing the computational cost. Experimental results are obtained using a synthetic motion-blurred image dataset and a real blurred image dataset for comparison with existing deblurring methods. The experimental results demonstrate that the image deblurring method proposed in this paper has a favorable effect on actual motion-blurred images.