Bicubic Research Articles

Super-resolution Crack Image Generation for Fixed PTZ-Camera Based Highway Pavement Crack Recognition

Abstract Among the various diseases that inevitably occur on expressways, cracks are the most common and are significant indicators of highway pavement damage. Timely and accurate crack recognition is urgently required for highway maintenance. Highway pavement crack recognition depends mainly on human visual inspection and expressway maintenance vehicles. These approaches are very time-consuming, labor-intensive, and difficult to implement in civil engineering. Cost-effective fixed PTZ (pan/tilt/zoom)-camera based crack recognition was investigated in our previous work. However, for pavement cracks captured at long camera distances, the limited resolution of the PTZ-vision results in low-resolution crack images. In addition, the cracks are weak features, such that the crack pixels density and distribution are significantly affected by the background noise, making it difficult to recognize these cracks. Aiming at to solve these problems, a high-order kernel based modified bicubic interpolation is proposed to typically reveal and characterize discrete pixel variations, obtaining high-quality super-resolution crack images, and improving the recognition performance of cracks. Extensive experiments in relation to crack datasets captured by PTZ-cameras on G4/highways in China are conducted to verify the performance of the proposed method. Two measurement parameters Just Noticeable Blue (JNB) and Structural Similarity Index (SSIM) confirm the high-quality of the super-resolution crack images. Experimental comparisons demonstrate that super-resolution crack images based crack recognition achieves out-performance, such that the mAP, precision (P), recall (R), and F1-score are increased to $95.3\%, 97.3\%, 96.1\%$, and $97.4\%$, respectively. This method proves the feasibility of high-efficiency crack recognition using modified bicubic interpolation for fixed PTZ-vision based expressways maintenance engineering.

Rapid Acquisition of High-Pixel Fluorescence Lifetime Images of Living Cells via Image Reconstruction Based on Edge-Preserving Interpolation.

Fluorescence lifetime imaging (FLIM) has established itself as a pivotal tool for investigating biological processes within living cells. However, the extensive imaging duration necessary to accumulate sufficient photons for accurate fluorescence lifetime calculations poses a significant obstacle to achieving high-resolution monitoring of cellular dynamics. In this study, we introduce an image reconstruction method based on the edge-preserving interpolation method (EPIM), which transforms rapidly acquired low-resolution FLIM data into high-pixel images, thereby eliminating the need for extended acquisition times. Specifically, we decouple the grayscale image and the fluorescence lifetime matrix and perform an individual interpolation on each. Following the interpolation of the intensity image, we apply wavelet transformation and adjust the wavelet coefficients according to the image gradients. After the inverse transformation, the original image is obtained and subjected to noise reduction to complete the image reconstruction process. Subsequently, each pixel is pseudo-color-coded based on its intensity and lifetime, preserving both structural and temporal information. We evaluated the performance of the bicubic interpolation method and our image reconstruction approach on fluorescence microspheres and fixed-cell samples, demonstrating their effectiveness in enhancing the quality of lifetime images. By applying these techniques to live-cell imaging, we can successfully obtain high-pixel FLIM images at shortened intervals, facilitating the capture of rapid cellular events.

A diffusion-based super resolution model for enhancing sonar images.

Improved hardware and processing techniques such as synthetic aperture sonar have led to imaging sonar with centimeter resolution. However, practical limitations and old systems limit the resolution in modern and legacy datasets. This study proposes using single image super resolution based on a conditioned diffusion model to map between images at different resolutions. This approach focuses on upscaling legacy, low-resolution sonar datasets to enable backward compatibility with newer, high-resolution datasets, thus creating a unified dataset for machine learning applications. The study demonstrates improved performance for classifying upscaled images without increasing the probability of false detection. The increased probability of detection was 7% compared to bicubic interpolation, 6% compared to convolutional neural networks, and 2% compared to generative adversarial networks. The study also proposes two sonar specific evaluation metrics based on acoustic physics and utility to automatic target recognition.

Interpolation of Imaging Mass Spectrometry Data by a Window-Based Adversarial Autoencoder Method.

Imaging mass spectrometry (IMS) is a technique for simultaneously acquiring the expression and distribution of molecules on the surface of a sample, and it plays a crucial role in spatial omics research. In IMS, the time cost and instrument load required for large data sets must be considered, as IMS typically involves tens of thousands of pixels or more. In this study, we developed a high-resolution method for IMS data reconstruction using a window-based Adversarial Autoencoder (AAE) method. We acquired IMS data from partial cerebellum regions of mice with a pitch size of 75 μm and then down-sampled the data to a pitch size of 150 μm, selecting 22 m/z peak intensity values per pixel. We established an AAE model to generate three pixels from the surrounding nine pixels within a window to reconstruct the image data at a pitch size of 75 μm. Compared with two alternative interpolation methods, Bilinear and Bicubic interpolation, our window-based AAE model demonstrated superior performance on image evaluation metrics for the validation data sets. A similar model was constructed for larger mouse kidney tissues, where the AAE model achieved high image evaluation metrics. Our method is expected to be valuable for IMS measurements of large animal organs across extensive areas.

Ground-Based Cloud Image Segmentation Method Based on Improved U-Net

Cloud image segmentation is a technique that divides images captured by meteorological satellites or ground-based observations into different regions or categories. By extracting the distribution, shape, and dynamic features of clouds, it provides precise data support for the meteorological and environmental fields, significantly influencing photovoltaic (PV) power generation forecasting, astronomical telescope observatory site selection, and weather forecasting. A ground-based cloud image segmentation model based on an improved U-Net is proposed, which adopts an overall encoder–decoder structure. In the encoder phase, this paper constructs a dilated convolution–atrous spatial pyramid pooling (ASPP)–dilated convolution structure to enhance early cloud feature extraction. Dilated convolution is a novel type of convolution that expands the receptive field by inserting holes into standard convolution, thereby capturing a larger range of contextual information. ASPP maintains high resolution while paying attention to both local details and global structures of the image. In the decoder stage, the bicubic interpolation method is used for up-sampling to restore the feature map resolution and improve the clarity of the segmented image. The bicubic interpolation method refers to the use of cubic polynomial functions to interpolate the pixel values of the input image. In addition, this paper designs a novel skip connection layer structure between the encoder and decoder, composed of a depthwise separable path (DS path) and an improved channel spatial attention module (Im-CSAM) connected in sequence. The DS path combines depthwise separable convolutions and residual structures to facilitate information exchange between high-level and low-level features. The Im-CSAM is a modular attention mechanism that focuses on important cloud features in spatial and channel dimensions to enhance segmentation accuracy. Experiments show that compared to the traditional U-Net, the accuracy, precision, and MIoU of this model improved by 2.2%, 4.1%, and 5.0%, respectively, in the SWINySEG dataset, and by 3.2%, 3.6%, and 5.8%, respectively, in the TCDD dataset, proving that the improved method has a better generalization ability and segmentation performance.

Neutron-Image Super-Resolution based on Convolutional Neural Networks and Novel Parallel Transformer

Enhancing the resolution of neutron imaging can be achieved by upgrading the hardware components of the neutron imaging system, including employing more potent neutron source systems, utilizing high-resolution detectors, and implementing superior collimation techniques. However, such improvements are frequently constrained by challenges such as high cost and limited flexibility. In this paper, a flexible and efficient single-neutron-image super-resolution network is proposed for neutron image super-resolution. We propose a novel multi-scale feature extraction network, which can extract features deeply by fusing features of different types and levels. In order to learn more context information, a novel parallel Transformer is proposed to overcome the long dependency problem of sequences. A high-frequency information enhancement network is proposed to enhance the high-frequency information of neutron image in order to address the blur problem. Quantitative and qualitative analyses of benchmark datasets and real neutron images show that the proposed network performs better on Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) than bicubic interpolation and other methods, and reconstructs high-resolution neutron images that are more consistent with human visual perception, highlighting its application potential in neutron images quality enhancement.

A local and global feature fusion network for Super-Resolution reconstruction of turbulent flows

The resolution of flow fields represents a significant factor influencing the accuracy of turbulent flow analysis. Nevertheless, the acquisition of high-resolution turbulence data remains a challenge due to the limitations imposed by computing resources. The interpolation method, while capable of achieving high-resolution turbulence at low cost, faces challenges in capturing details of turbulent flows. In this study, a local and global feature fusion network (LGFN) is designed for the reconstruction of high-resolution turbulent flows with high quality. First, dual parallel branches made of dense blocks are introduced into the LGFN to extract local features of turbulent flows. Moreover, the features after the first dense block of each branch are summarized into the self-attention block to obtain global features. The extracted local and global features are aggregated through learnable weight parameters to achieve feature fusion. Finally, the fused turbulence features are scaled to the same dimensional size as the high-resolution turbulence through the implementation of multilayer pixel shuffle layers and convolution layers. The effectiveness of the proposed network was evaluated using datasets of forced isotropic turbulence and channel turbulence. The results demonstrate that the reconstructed velocity fields of the LGFN exhibit the highest degree of similarity to the direct numerical simulation results, in comparison with bicubic interpolation, static convolutional neural network, and super-resolution dense connection network results. In addition, compared to alternative methods, the proposed network effectively captures the characteristics of isotropic or anisotropic turbulence even at larger scales.

Data-driven Prediction of Underwater Navigation Fitness Zone Classification

This article presents a pioneering approach to enhancing underwater navigation accuracy and stealth through the utilization of gravity anomaly data. Developed by researchers from leading universities in China, the study aims to revolutionize autonomous marine navigation systems in unknown sea areas. By integrating advanced data processing, feature extraction, model construction, and optimization techniques, a cutting-edge navigation system has been formulated, showcasing remarkable improvements in navigation precision.The core innovation lies in leveraging bicubic interpolation to refine data resolution, enabling a meticulous evaluation of regional suitability and segmentation of sea zones. Subsequently, a gradient-based suitability prediction model is constructed, which is validated through migration prediction, underscoring its effectiveness and reliability. This research not only addresses the limitations of traditional terrain matching navigation methods, plagued by data accuracy and resolution constraints, but also paves the way for advancements in oceanic exploration and technological competitiveness.The introduction of gravity anomaly data, coupled with machine learning and visualization tools, marks a significant step forward in the development of adaptive navigation zones. This innovative system holds immense potential to enhance the autonomy, accuracy, and concealment of underwater vehicles, thereby facilitating safer and more efficient oceanic missions in the future.

Increased virtual resolution for sub-pixel displacement algorithm optimization in digital image correlation for AISI 1020 steel

The development of the digital image correlation (DIC) technique has required the use of cameras with increasingly higher resolution, as well as requiring complex sub-pixel tracking algorithms with exponentially increasing computational cost. This article proposes a DIC algorithm based on the correlation coefficient curve-fitting method, minimizing SSD or ZNSSD correlation criteria, with the help of increasing the virtual resolution of the images through bicubic interpolation, for obtain an accurate, robust and reliable algorithm according to the highest computational efficiency. The developed algorithm was virtually validated using synthetic images and experimentally using AISI 1020 steel samples test in tension, performed with strain gauges. The results showed a reduction in the error observed in synthetic images with increased magnification and good agreement with the deformation data obtained using the conventional extensometry technique.

Integrating hydrological knowledge into deep learning for DEM super-resolution

Deep learning-based super-resolution methods have been successfully applied to digital elevation model (DEM) downscaling studies by designing structures and loss functions of the model. However, little attention has been paid to the design of super-resolution models that can maintain the hydrological characteristics of the DEM, which is important for hydrological studies. This study introduces a super-resolution model that integrates hydrologic knowledge (HKSRCGAN), with the aim to effectively maintain topographic features as well as the hydrologic connectivity of the DEM. The hydrological knowledge derived from surface flow direction and hydrological features are integrated into a deep learning algorithm to guide model training. The 30 m spatial resolution FABDEM is used to demonstrate the utility of the proposed method. Results show that the HKSRCGAN outperforms the bicubic interpolation, SRCNN, SRGAN, SRResNet and TfaSR methods in reducing topographic errors and maintaining hydrologic characteristics. In the test area, the entropy difference analysis shows that the DEM generated by HKSRCGAN is similar to the information contained in the reference DEM. Furthermore, super-resolution models integrating hydrological knowledge are valuable for modeling terrain primarily shaped by gravity and surface water flows. In the future, deep learning-based models integrating hydrologic knowledge are expected to be applied in DEM upscaling to maintain consistent hydrological characteristics.

Outcomes of CEM43 in Predicting Thermal Damage Induced by Focal Laser Ablation in Controlled Ex Vivo Experiments: A Comparison to Histology and MRI.

Focal laser ablation (FLA) serves as a targeted therapy for prostate cancer (PCa). Clinical studies have demonstrated significant variations in ablation volumes with consistent fiber configurations. Consequently, a prediction model is needed for the safe application of FLA in treating PCa. This study aimed to evaluate the reproducibility of FLA-induced temperature profiles in controlled ex vivo experiments using clinical laser treatment protocols. Additionally, it sought to examine the effectiveness of the CEM43 model in predicting the zone of irreversible damage (ZID) and to compare these findings with outcomes derived from the Arrhenius model. Freshly excised postmortem human prostate and porcine liver specimens were used for controlled ex vivo ablation. Tissues were secured in a Perspex sample holder for precise placement of the laser fiber and thermocouples. FLA was conducted with a 1064-nm Nd:YAG laser at 3 W in continuous-wave mode for 10 min. Pre- and post-FLA 3D T1-weighted 7 T MRI scans were obtained to assess the treatment area. Whole-mount hematoxylin and eosin histological slides were prepared and digitized. On histology, the ZID was defined as the total of vaporized, carbonized, and coagulated tissue. A 2D thermal development map was created from temperature data, using bi-cubic interpolation. The cumulative equivalent thermal isoeffect dose at 43°C in minutes (CEM43) model was applied to predict the ZID, with 240 equivalent minutes (240-CEM43) used as the damage threshold. Additionally, the Arrhenius thermal model was used for comparison of CEM43 results. Predicted ZIDs were compared to MRI and histology. FLA treatment was performed on ex vivo human prostate samples (n = 2) and porcine liver specimens (n = 5). For human prostate tissue, FLA did not result in an identifiable ZID upon histological macroscopic examination or a lesion on MRI. Ex vivo porcine liver samples showed a clearly demarcated oval-shaped hyperintense lesion surrounding the laser fiber tip on post-FLA MRI. The MRI lesion (range 1.6-2.1 cm2) corresponded with the shape and location of the ZID on histology, but was smaller (median 1.7 vs. 3.2, p = 0.02). Histological examination of porcine liver samples revealed ZIDs ranging from 2.1 to 4.1 cm2, whereas 240-CEM43-predicted ZIDs ranged from 3.3 to 3.8 cm2. Although the median 240-CEM43-predicted ZID was not significantly larger than the histology ZID (3.8 vs. 3.2 cm2, p = 0.22), it tended to overpredict the histological results in most experiments. The median Arrhenius-predicted ZID was similar to the histologicalZID (3.2 vs. 3.2 cm2, p = 0.56), but varied in size when comparing individual experiments (range 2.5-3.2 cm2). FLA on ex vivo human prostate showed no thermal damage on histopathology or MRI. Ex vivo porcine liver FLA resulted in identifiable ZID on histology and lesions on MRI. 240-CEM43 generally overestimated the ZID and had less variability compared to histology. Results from the Arrhenius model were in better agreement with the histology findings, but still did not predict the individual FLA-induced histological thermal damage. Inter-experiment ZID variability underlines the need for developing a more comprehensive predictive dosimetry model for FLA in PCa treatment.

Stereo Bi-Telecentric Phase-Measuring Deflectometry.

Replacing the endocentric lenses in traditional Phase-Measuring Deflectometry (PMD) with bi-telecentric lenses can reduce the number of parameters to be optimized during the calibration process, which can effectively increase both measurement precision and efficiency. Consequently, the low distortion characteristics of bi-telecentric PMD contribute to improved measurement accuracy. However, the calibration of the extrinsic parameters of bi-telecentric lenses requires the help of a micro-positioning stage. Using a micro-positioning stage for the calibration of external parameters can result in an excessively cumbersome and time-consuming calibration process. Thus, this study proposes a holistic and flexible calibration solution for which only one flat mirror in three poses is needed. In order to obtain accurate measurement results, the calibration residuals are utilized to construct the inverse distortion map through bicubic Hermite interpolation in order to obtain accurate anchor positioning result. The calibrated stereo bi-telecentric PMD can achieve 3.5 μm (Peak-to-Valley value) accuracy within 100 mm (Width) × 100 mm (Height) × 200 mm (Depth) domain for various surfaces. This allows the obtaining of reliable measurement results without restricting the placement of the surface under test.

Subpixel water area mapping based on spatial information processing at superpixel scale for multispectral imagery

ABSTRACT Subpixel water area mapping based on spatial information processing at superpixel scale (SISS) for multispectral imagery is proposed in this paper. In SISS, the superpixels with irregular shape are first obtained by segmenting the first principal component of the improved imagery which is improved from the the original coarse multispectral imagery by using bicubic interpolation; The random walk algorithm is then introduced to calculate these superpixels to obtain the spatial information. Finally, according to this spatial information, the label of the water area or the non-water area is assigned to each subpixel by the class allocation method, producing the final mapping result. The experimental results on two datasets show that the proposed method can achieve the best performance in both visual effects and quantitative indicators.

Deep Learning Applied to Imbalanced Malware Datasets Classification

In the current day, the evolution and exponential proliferation of malware involve modifications and camouflage of their structure through techniques like obfuscation, polymorphism, metamorphism, and encryption. With the advancements in deep learning, methods such as convolutional neural networks (CNN) have emerged as potent tools for deciphering intricate patterns within this malicious software. The present research uses the capacity of CNN to learn the global structure of the code converted to an RGB or grayscale image and decipher the patterns present in the malware datasets generated from these images. The study explores fine-tuning techniques, including bicubic interpolation, ReduceLROnPlateau, and class weight estimation, in order to generalize the model and reduce the risk of overfitting for malware that uses evasion techniques against classification. Taking advantage of transfer learning and the MobileNet architecture, we created a MobileNet fine-tuning (FT) model. The application of this new model in four datasets, including Microsoft Big 2015, Malimg, MaleVis, and a new Fusion dataset, achieved 98.71%, 99.08%, 96.04%, and 98.04% accuracy, respectively, which underscores the robustness of the proposed model. The Fusion dataset is a combination of the first three datasets, consisting of a set of 32,601 known malware image files representing a mix of 59 different families. Despite the success, the study reveals performance deterioration with an increase in the number of malware families, highlighting the need for further exploration into the limits of CNNs in malware classification.

Detection of Abnormal Blood Flow Region Based on Near Infrared Correlation Spectroscopy

Blood flow measurement of microvessels in human tissues is of vital importance for the diagnosis and treatment of many diseases. In this paper, the detection method of abnormal blood flow regions based on near-infrared correlation spectroscopy is studied. We used the NL-Bregman-TV imaging algorithm to realize Blood flow imaging. However, due to the limitation of the number and distribution of detectors, the pixels obtained from images are extremely low, which cannot meet the practical requirements of the visual and the abnormal blood flow range measurement. In this paper, the bicubic interpolation method is used to improve the resolution of low-pixel blood flow images. The parameter index of the normalized similarity was proposed to help judge the effect of the interpolation method on the resolution of this kind of image. Aiming at the extraction of abnormal regions, a threshold segmentation algorithm based on the histogram difference method and a morphological processing algorithm is proposed to extract the contour of abnormal blood flow. The method proposed in this paper can be used to accurately locate and extract the clear and smooth contour of abnormal blood flow.

Generative adversarial networks with deep blind degradation powered terahertz ptychography

Ptychography is an imaging technique that uses the redundancy of information generated by the overlapping of adjacent light regions to calculate the relative phase of adjacent regions and reconstruct the image. In the terahertz domain, in order to make the ptychography technology better serve engineering applications, we propose a set of deep learning terahertz ptychography system that is easier to realize in engineering and plays an outstanding role. To address this issue, we propose to use a powerful deep blind degradation model which uses isotropic and anisotropic Gaussian kernels for random blurring, chooses the downsampling modes from nearest interpolation, bilinear interpolation, bicubic interpolation and down-up-sampling method, and introduces Gaussian noise, JPEG compression noise, and processed detector noise. Additionally, a random shuffle strategy is used to further expand the degradation space of the image. Using paired low/high resolution images generated by the deep blind degradation model, we trained a multi-layer residual network with residual scaling parameters and dense connection structure to achieve the neural network super-resolution of terahertz ptychography for the first time. We use two representative neural networks, SwinIR and RealESRGAN, to compare with our model. Experimental result shows that the proposed method achieved better accuracy and visual improvement than other terahertz ptychographic image super-resolution algorithms. Further quantitative calculation proved that our method has significant advantages in terahertz ptychographic image super-resolution, achieving a resolution of 33.09 dB on the peak signal-to-noise ratio (PSNR) index and 3.05 on the naturalness image quality estimator (NIQE) index. This efficient and engineered approach fills the gap in the improvement of terahertz ptychography by using neural networks.

Benchmarking Time-Frequency Representations of Phonocardiogram Signals for Classification of Valvular Heart Diseases Using Deep Features and Machine Learning

Heart sounds and murmur provide crucial diagnosis information for valvular heart diseases (VHD). A phonocardiogram (PCG) combined with modern digital processing techniques provides a complementary tool for clinicians. This article proposes a benchmark different time–frequency representations, which are spectograms, mel-spectograms and cochleagrams for obtaining images, in addition to the use of two interpolation techniques to improve the quality of the images, which are bicubic and Lanczos. Deep features are extracted from a pretrained model called VGG16, and for feature reduction, the Boruta algorithm is applied. To evaluate the models and obtain more precise results, nested cross-validation is used. The best results achieved in this study were for the cochleagram with 99.2% accuracy and mel-spectogram representation with the bicubic interpolation technique, which reached 99.4% accuracy, both having a support vector machine (SVM) as a classifier algorithm. Overall, this study highlights the potential of time–frequency representations of PCG signals combined with modern digital processing techniques and machine learning algorithms for accurate diagnosis of VHD.

Automated high-precision alignment of geologic maps with well orientation

The 3D reflection seismic interpretation entails the creation of structure maps of subsurface horizons across extensive areas, often spanning tens of thousands of square kilometers within sedimentary basins. To be correlated with well measurements, maps made from 3D reflection seismic are routinely assigned the correct depth at well control points. However, integrating orientation information measured from wells remains a challenge due to the absence of upscaling guidelines. We address the scale-dependent nature of geologic structure and well orientation information in subsurface mapping, particularly focusing on integrating kilometer-scale seismic interpretation with centimeter-scale well-measured dip and azimuth data. Our pioneering methodology streamlines this integration process through three essential steps: first, transforming well dip and azimuth into a gradient representation; second, smoothly extending gradient differences between the map and well within a user-defined radius of influence; and third, constructing an updated horizon map by bicubic spline interpolation to ensure automated and high-precision alignment. In addition, a structure-oriented interpolation technique is introduced to preserve faults and local structures during orientation correction. Application of the methodology to a 3D reflection seismic horizon demonstrates its effectiveness in automating the complex task of tying subsurface maps to well orientation information. This not only reduces the need for manual interventions but also introduces a new perspective on subsurface mapping. It provides a robust framework that enhances the accuracy and reliability of geologic interpretation, offering significant advancements beyond previous efforts in the literature.

Evaluation of extra pixel interpolation with mask processing for medical image segmentation with deep learning

Current mask processing operations rely on interpolation algorithms that do not produce extra pixels, such as nearest neighbor (NN) interpolation, as opposed to algorithms that do produce extra pixels, like bicubic (BIC) or bilinear (BIL) interpolation. In our previous study, the author proposed an alternative approach to NN-based mask processing and evaluated its effects on deep learning training outcomes. In this study, the author evaluated the effects of both BIC-based image and mask processing and BIC-and-NN-based image and mask processing versus NN-based image and mask processing. The evaluation revealed that the BIC-BIC model/network was an 8.9578% (with image size 256 × 256) and a 1.0496% (with image size 384 × 384) increase of the NN-NN network compared to the NN-BIC network which was an 8.3127% (with image size 256 × 256) and a 0.2887% (with image size 384 × 384) increase of the NN-NN network.

A Remote Sensing Image Super-Resolution Reconstruction Model Combining Multiple Attention Mechanisms.

Remote sensing images are characterized by high complexity, significant scale variations, and abundant details, which present challenges for existing deep learning-based super-resolution reconstruction methods. These algorithms often exhibit limited convolutional receptive fields and thus struggle to establish global contextual information, which can lead to an inadequate utilization of both global and local details and limited generalization capabilities. To address these issues, this study introduces a novel multi-branch residual hybrid attention block (MBRHAB). This innovative approach is part of a proposed super-resolution reconstruction model for remote sensing data, which incorporates various attention mechanisms to enhance performance. First, the model employs window-based multi-head self-attention to model long-range dependencies in images. A multi-branch convolution module (MBCM) is then constructed to enhance the convolutional receptive field for improved representation of global information. Convolutional attention is subsequently combined across channels and spatial dimensions to strengthen associations between different features and areas containing crucial details, thereby augmenting local semantic information. Finally, the model adopts a parallel design to enhance computational efficiency. Generalization performance was assessed using a cross-dataset approach involving two training datasets (NWPU-RESISC45 and PatternNet) and a third test dataset (UCMerced-LandUse). Experimental results confirmed that the proposed method surpassed the existing super-resolution algorithms, including Bicubic interpolation, SRCNN, ESRGAN, Real-ESRGAN, IRN, and DSSR in the metrics of PSNR and SSIM across various magnifications scales.