Super-resolution Reconstruction Algorithm Research Articles

Super-resolution Inversion and Reconstruction for Remote Sensing Images of Bands of Interest

The super-resolution inversion and reconstruction algorithm of remote sensing image of band of interest is proposed in this paper. Based on the existing multi-resolution hyperspectral images, a spectral reflectance dictionary is constructed, according to which the objects in radiation calibrated and atmospheric corrected scene spectral reflectance images are sorted and classified using the end element extraction algorithm and the correlation distance method. Based on the classification results, a dictionary of scene feature spectral reflectance is constructed. Based on this, the sparse representation method and the secondary optimization algorithm based on image similarity are used to solve and optimize the feature distribution of the scene image. According to the feature distribution of the scene image, using the remote sensing link imaging model, super-resolution inversion reconstruction is performed to obtain a high-resolution remote sensing image of a band of interest. Experimental results show that the proposed algorithm can reconstruct low-resolution remote-sensing images of different bands into high-resolution remote-sensing images. Thus, the proposed algorithm can effectively boost image resolution, enrich image details and improve the target detection capability by the image.

Deep USRNet Reconstruction Method Based on Combined Attention Mechanism

Single image super-resolution (SISR) based on deep learning is a key research problem in the field of computer vision. However, existing super-resolution reconstruction algorithms often improve the quality of image reconstruction through a single network depth, ignoring the problems of reconstructing image texture structure and easy overfitting of network training. Therefore, this paper proposes a deep unfolding super-resolution network (USRNet) reconstruction method under the integrating channel attention mechanism, which is expected to improve the image resolution and restore the high-frequency information of the image. Thus, the image appears sharper. First, by assigning different weights to features, focusing on more important features and suppressing unimportant features, the details such as image edges and textures are better recovered, and the generalization ability is improved to cope with more complex scenes. Then, the CA (Channel Attention) module is added to USRNet, and the network depth is increased to better express high-frequency features; multi-channel mapping is introduced to extract richer features and enhance the super-resolution reconstruction effect of the model. The experimental results show that the USRNet with integrating channel attention has a faster convergence rate, is not prone to overfitting, and can be converged after 10,000 iterations; the average peak signal-to-noise ratios on the Set5 and Set12 datasets after the side length enlarged by two times are, respectively, 32.23 dB and 29.72 dB, and are dramatically improved compared with SRCNN, SRMD, PAN, and RCAN. The algorithm can generate high-resolution images with clear outlines, and the super-resolution effect is better.

Super-Resolution Reconstruction Algorithm-Based MRI Diagnosis of Prostate Cancer and Evaluation of Treatment Effect of Prostate Specific Antigen

MRI of prostate cancer (PCa) was performed using a projection onto convex sets (POCS) super-resolution reconstruction algorithm to evaluate and analyze the treatment of prostate-specific antigen (PSA) and provide a theoretical reference for clinical practice. A total of 110 patients with PCa were selected as the study subjects. First, the modified POCS algorithm was used to reconstruct the MRI images, and the gradient interpolation algorithm was used instead of the traditional bilinear algorithm to preserve the edge information. The diagnostic and therapeutic effects of MRI examination, PSA examination, and MRI combined with PSA based on a super-resolution reconstruction algorithm were then compared. The simulation results showed that the POCS algorithm was superior to the bilinear interpolation results and was superior to the common POCS algorithm. After adding noise artificially, the restoration algorithm was effective and could preserve the details in the image. The performance indexes of PSA in the diagnosis of PCa were 75.4%, 60.1%, 70.08%, 72.2%, and 60.3%, respectively; the performance indexes of MRI in the diagnosis of PCa were 84.6%, 61.4%, 71.11%, 73.08%, and 61.9%, respectively; and the performance indexes of MRI combined with PSA based on the super-resolution reconstruction algorithm in the diagnosis of PCa were 96.05%, 88.3%, 95.1%, 93.6%, and 92.7%, respectively. The indicators of MRI combined with PSA based on the super-resolution reconstruction algorithm were significantly higher than those of the other two methods ( P < 0.05). The signal-to-noise ratio of MRI of PCa based on the super-resolution reconstruction algorithm has been greatly improved, with good clarity, which can improve the diagnostic accuracy of PCa patients and has certain advantages in the examination. MRI based on the super-resolution reconstruction algorithm has a high value in the diagnosis and treatment of PCa.

Artificial Intelligence-Assisted Terahertz Imaging for Rapid and Label-Free Identification of Efficient Light Formula in Laser Therapy.

Photodynamic therapy (PDT) is considered a promising noninvasive therapeutic strategy in biomedicine, especially by utilizing low-level laser therapy (LLLT) in visible and near-infrared spectra to trigger biological responses. The major challenge of PDT in applications is the complicated and time-consuming biological methodological measurements in identification of light formulas for different diseases. Here, we demonstrate a rapid and label-free identification method based on artificial intelligence (AI)-assisted terahertz imaging for efficient light formulas in LLLT of acute lung injury (ALI). The gray histogram of terahertz images is developed as the biophysical characteristics to identify the therapeutic effect. Label-free terahertz imaging is sequentially performed using rapid super-resolution imaging reconstruction and automatic identification algorithm based on a voting classifier. The results indicate that the therapeutic effect of LLLT with different light wavelengths and irradiation times for ALI can be identified using this method with a high accuracy of 91.22% in 33 s, which is more than 400 times faster than the biological methodology and more than 200 times faster than the scanning terahertz imaging technology. It may serve as a new tool for the development and application of PDT.

Application of a Deep Learning Algorithm for Combined Super-Resolution and Partial Fourier Reconstruction Including Time Reduction in T1-Weighted Precontrast and Postcontrast Gradient Echo Imaging of Abdominopelvic MR Imaging.

Purpose: The purpose of this study was to test the technical feasibility and the impact on the image quality of a deep learning-based super-resolution reconstruction algorithm in 1.5 T abdominopelvic MR imaging. Methods: 44 patients who underwent abdominopelvic MRI were retrospectively included, of which 4 had to be subsequently excluded. After the acquisition of the conventional volume interpolated breath-hold examination (VIBEStd), images underwent postprocessing, using a deep learning-based iterative denoising super-resolution reconstruction algorithm for partial Fourier acquisitions (VIBESR). Image analysis of 40 patients with a mean age of 56 years (range 18–84 years) was performed qualitatively by two radiologists independently using a Likert scale ranging from 1 to 5, where 5 was considered the best rating. Results: Image analysis showed an improvement of image quality, noise, sharpness of the organs and lymph nodes, and sharpness of the intestine for pre- and postcontrast images in VIBESR compared to VIBEStd (each p < 0.001). Lesion detectability was better for VIBESR (p < 0.001), while there were no differences concerning the number of lesions. Average acquisition time was 16 s (±1) for the upper abdomen and 15 s (±1) for the pelvis for VIBEStd, and 15 s (±1) for the upper abdomen and 14 s (±1) for the pelvis for VIBESR. Conclusion: This study demonstrated the technical feasibility of a deep learning-based super-resolution algorithm including partial Fourier technique in abdominopelvic MR images and illustrated a significant improvement of image quality, noise, and sharpness while reducing TA.

Research on Super-Resolution Relationship Extraction and Reconstruction Methods for Images Based on Multimodal Graph Convolutional Networks

This study constructs a multimodal graph convolutional network model, conducts an in-depth study on image super-resolution relationship extraction and reconstruction methods, and constructs a model of image super-resolution relationship extraction and reconstruction methods based on multimodal graph convolutional networks. In this study, we study the domain adaptation model algorithm based on chart convolutional networks, which constructs a global relevance graph based on all samples using pre-extracted features and performs distribution approximation of sample features in two domains using a diagram convolutional neural network with maximum mean difference loss; with this approach, the model effectively preserves the structural information among the samples. In this study, several comparison experiments are designed based on the COCO and VG datasets; the image space information-based and knowledge graph-based target detection and recognition models substantially improve recognition performance over the baseline model. The super-pixel-based target detection and recognition model can also effectively reduce the number of floating-point operations and the complexity of the model. In this study, we propose a multiscale GAN-based image super-resolution reconstruction algorithm. Aiming at the problems of detail loss or blurring in the reconstruction of detail-rich images by SRGAN, it integrates the idea of the Laplace pyramid to complete the task of multiscale reconstruction of images through staged reconstruction. It incorporates the concept of a discriminative network with patch GAN to effectively improve the recovery effect of graph details and improve the reconstruction quality of images. Using Set5, Set14, BSD100, and Urban100 datasets as test sets, experimental analysis is conducted from objective and subjective evaluation metrics to effectively validate the performance of the improved algorithm proposed in this study.

Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning

Review of Image Super-resolution Reconstruction Algorithms Based on Deep Learning

The influence of digital twins on the methods of film and television creation

All walks of life in the world have entered the "big data era", and China's film and television industry is also developing rapidly. Digital twins can also have a huge impact on how film and television are created. This paper aims to study the theoretical knowledge of digital twins and film and television creation methods, and propose an image and video super-resolution reconstruction algorithm based on video computing and reconstruction. Both algorithms make perfect sense in digital twins. This paper proposes how to study the impact of digital twins on the way of film and television creation. It can be seen from the data in Table 1 of the experiment in this paper that the director's attention to the way of film and television creation has increased from 54% in 2013 to 70% in 2019. It can be seen that the decoding quality of the two reconstruction algorithms is higher than that of the traditional reconstruction algorithm. It can be seen that it is very meaningful to explore the impact of digital twins on the way of film and television creation.Digital twin is a multi-disciplinary, multi-physical, multi-scale, multi-probability simulation process that makes full use of physical models, sensor updates, and operating history to complete the mapping in virtual space, thereby reflecting the full life cycle of the corresponding physical equipment. process. A digital twin is a concept beyond reality that can be viewed as a digital mapping system of one or more important, interdependent equipment systems.

Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network.

The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.

Combined Deep Learning-based Super-Resolution and Partial Fourier Reconstruction for Gradient Echo Sequences in Abdominal MRI at 3 Tesla: Shortening Breath-Hold Time and Improving Image Sharpness and Lesion Conspicuity

To investigate the impact of a prototypical deep learning-based super-resolution reconstruction algorithm tailored to partial Fourier acquisitions on acquisition time and image quality for abdominal T1-weighted volume-interpolated breath-hold examination (VIBESR) at 3 Tesla. The standard T1-weighted images were used as the reference standard (VIBESD). Patients with diverse abdominal pathologies, who underwent a clinically indicated contrast-enhanced abdominal VIBE magnetic resonance imaging at 3T between March and June 2021 were retrospectively included. Following the acquisition of the standard VIBESD sequences, additional images for the non-contrast, dynamic contrast-enhanced and post-contrast T1-weighted VIBE acquisition were retrospectively reconstructed using the same raw data and employing a prototypical deep learning-based super-resolution reconstruction algorithm. The algorithm was designed to enhance edge sharpness by avoiding conventional k-space filtering and to perform a partial Fourier reconstruction in the slice phase-encoding direction for a predefined asymmetric sampling ratio. In the retrospective reconstruction, the asymmetric sampling was realized by omitting acquired samples at the end of the acquisition and therefore corresponding to a shorter acquisition. Four radiologists independently analyzed the image datasets (VIBESR and VIBESD) in a blinded manner. Outcome measures were: sharpness of abdominal organs, sharpness of vessels, image contrast, noise, hepatic lesion conspicuity and size, overall image quality and diagnostic confidence. These parameters were statistically compared and interrater reliability was computed using Fleiss' Kappa and intraclass correlation coefficient (ICC). Finally, the rate of detection of hepatic lesions was documented and was statistically compared using the paired Wilcoxon test. A total of 32 patients aged 59 ± 16 years (23 men (72%), 9 women (28%)) were included. For VIBESR, breath-hold time was significantly reduced by approximately 13.6% (VIBESR 11.9 ± 1.2 seconds vs. VIBESD: 13.9 ± 1.4 seconds, p < 0.001). All readers rated sharpness of abdominal organs, sharpness of vessels to be superior in images with VIBESR (p values ranged between p=0.005 and p < 0.001). Despite reduction of acquisition time, image contrast, noise, overall image quality and diagnostic confidence were not compromised, as there was no evidence of a difference between VIBESR and VIBESD (p > 0.05). The inter-reader agreement was substantial with a Fleiss' Kappa of >0.7 in all contrast phases. A total of 13 hepatic lesions were analyzed. The four readers observed a superior lesion conspicuity in VIBESR than in VIBESD (p values ranged between p=0.046 and p < 0.001). In terms of lesion size, there was no significant difference between VIBESD and VIBESR for all readers. Finally, there was an excellent inter-reader agreement regarding lesion size (ICC > 0.9). For all readers, no statistically significant difference was observed regarding detection of hepatic lesions between VIBESD and VIBESR. The deep learning-based super-resolution reconstruction with partial Fourier in the slice phase-encoding direction enabled a reduction of breath-hold time and improved image sharpness and lesion conspicuity in T1-weighted gradient echo sequences in abdominal magnetic resonance imaging at 3 Tesla. Faster acquisition time without compromising image quality or diagnostic confidence was possible by using this deep learning-based reconstruction technique.

Complex "zero-shot" super-resolution reconstruction algorithm for THz imaging.

To deal with a terahertz (THz) super-resolution (SR) algorithm based on a convolutional neural network (CNN) without standard training datasets, a complex "zero-shot" SR (CZSSR) reconstruction algorithm is proposed according to the internal image statistics with a five-layer complex CNN model. Instead of relying on pre-training, the proposed method is of sound self-adaptability. Compared with real ZSSR, the peak SNR of CZSSR rose by about 0.94 dB, MSE decreased by 0.042, and SSIM increased by about 40% for the SR result of the measured data. The results show that the CZSSR method can solve the low-resolution problem of a THz imaging system and the shortage of datasets in THz SR based on CNN. Therefore, this research is of great significance for application in the fields of medical imaging and non-destructive detection.

Improved generative adversarial network for retinal image super-resolution

Background and objectiveThe retina is the only organ in the body that can use visible light for non-invasive observation. By analyzing retinal images, we can achieve early screening, diagnosis and prevention of many ophthalmological and systemic diseases, helping patients avoid the risk of blindness. Due to the powerful feature extraction capabilities, many deep learning super-resolution reconstruction networks have been applied to retinal image analysis and achieved excellent results. MethodsGiven the lack of high-frequency information and poor visual perception in the current reconstruction results of super-resolution reconstruction networks under large-scale factors, we present an improved generative adversarial network (IGAN) algorithm for retinal image super-resolution reconstruction. Firstly, we construct a novel residual attention block, improving the reconstruction results lacking high-frequency information and texture details under large-scale factors. Secondly, we remove the Batch Normalization layer that affects the quality of image generation in the residual network. Finally, we use the more robust Charbonnier loss function instead of the mean square error loss function and the TV regular term to smooth the training results. ResultsExperimental results show that our proposed method significantly improves objective evaluation indicators such as peak signal-to-noise ratio and structural similarity. The obtained image has rich texture details and a better visual experience than the state-of-the-art image super-resolution methods. ConclusionOur proposed method can better learn the mapping relationship between low-resolution and high-resolution retinal images. This method can be effectively and stably applied to the analysis of retinal images, providing an effective basis for early clinical treatment.

Moving Object Detection Based on Fusion of Depth Information and RGB Features.

The detection of moving objects is one of the key problems in the field of computer vision. It is very important to detect moving objects accurately and rapidly for automatic driving. In this paper, we propose an improved moving object detection method to overcome the disadvantages of the RGB information-only-based method in detecting moving objects that are susceptible to shadow interference and illumination changes by adding depth information. Firstly, a convolutional neural network (CNN) based on the color edge-guided super-resolution reconstruction of depth maps is proposed to perform super-resolution reconstruction of low-resolution depth images obtained by depth cameras. Secondly, the RGB-D moving object detection algorithm is based on fusing the depth information of the same scene with RGB features for detection. Finally, in order to evaluate the effectiveness of the algorithm proposed in this paper, the Middlebury 2005 dataset and the SBM-RGBD dataset are successively used for testing. The experimental results show that our super-resolution reconstruction algorithm achieves the best results among the six commonly used algorithms, and our moving object detection algorithm improves the detection accuracy by up to 18.2%, 9.87% and 40.2% in three scenes, respectively, compared with the original algorithm, and it achieves the best results compared with the other three recent RGB-D-based methods. The algorithm proposed in this paper can better overcome the interference caused by shadow or illumination changes and detect moving objects more accurately.

Inverted N-Type Lightweight Network Based on Back Projection for ImageSuper-Resolution Reconstruction

Aiming at the problem that the super-resolution reconstruction algorithms based on deep learning increase the complexity of the network model and cause a large number of parameters by increasing the depth of neural network to improve the super-resolution reconstruction performance, an inverted N-type lightweight network based on back projection for image super-resolution reconstruction is proposed. First, the initial convolutional block is used to extract the shallow features of low-resolution images. Secondly, the deep features of low-resolution images are extracted through two rounds of gradual model compression based on the inverted N-type network of back projection and two rounds of gradual model restoration of the inverted N-type network. Then, the extracted shallow features and deep features are combined by global residual learning and scaled up to the desired output reconstructed image size by using the upsampling module. Finally, the reconstruction module is used to reconstruct the super-resolution image. The results of experiments on the Set5, Set14, BSDS100 and Urban100 testing sets show that the proposed algorithm has a lighter network structure than other algorithms, and the reconstructed super-resolution images not only have higher peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), but also have better visual effects.

Superresolution Reconstruction Method of Software Remote Sensing Image Based on Convolutional Neural Network

In order to solve the problem of long training time for remote sensing image super-resolution reconstruction algorithm, a method for remote sensing image superresolution reconstruction based on convolutional neural network is proposed, which combines dense convolutional network, parallel CNN structure, and subpixel convolution. The features of low-resolution images are extracted using dense convolutional networks, parallel CNNs are used to reduce network parameters, and subpixel convolutions are used to complete feature reconstruction. The results show that the final PSNR value of the black curve with the number of iterations of the three methods in the training process is the highest 27.3, followed by the middle curve, and the worst curve is 27.0. It is proved that the method extracts more features, retains more image details, and improves the reconstruction effect of the image; it greatly reduces the parameters in the network and avoids the phenomenon of overfitting in the deep network.

An improved algorithm for superresolution reconstruction of ancient murals with a generative adversarial network based on asymmetric pyramid modules

Ancient Chinese murals are true portrayals of ancient Chinese life, but well-preserved murals are rare. Therefore, ancient mural preservation and repair are critical. To address the poor superresolution reconstruction of mural images with unclear textures and fuzzy details, we developed an improved generative adversarial network (GAN) algorithm based on asymmetric pyramid modules for ancient mural superresolution reconstruction. Asymmetric pyramid modules, which are composed of a series of dense compression units, were used to learn image features. To analyze the reconstructed image features, a perceptual loss function was integrated to optimize the model performance. The use of the improved algorithm for low-resolution mural images increased the image resolution while preserving their original feature details and textures, and the improvement effect was visually observed in terms of indices such as the peak signal-to-noise ratio and structural similarity. Compared with other superresolution-related algorithms, the proposed model increased the peak signal-to-noise ratio by 0.20–6.66 dB. The GAN-based mural superresolution reconstruction algorithm proposed in this study effectively improved the performance of reconstructed high-resolution mural images, which increases the significance of the reconstructed image for future research.

A DEM Super-Resolution Reconstruction Network Combining Internal and External Learning

The study of digital elevation model (DEM) super-resolution reconstruction algorithms has solved the problem of the need for high-resolution DEMs. However, the DEM super-resolution reconstruction algorithm itself is an inverse problem, and making full use of the DEM a priori information is an effective way to solve this problem. In our work, a new DEM super-resolution reconstruction method is proposed based on the complementary relationship between internally learned super-resolution reconstruction methods and externally learned super-resolution reconstruction methods. The method is based on the presence of a large amount of repetitive information within the DEM. Using an internal learning approach to learn the internal prior of the DEM, a low-resolution dataset of the DEM rich in detailed features is generated, and based on this, the training of a constrained external learning network is constructed for the discrepancy data pair. Finally, it introduces residual learning based on the network model to accelerate the operation rate of the network and to solve the model degradation problem brought about by the deepening of the network. This enables the better transfer of learned detailed features in deeper network mappings, which in turn ensures accurate learning of the DEM prior information. The network utilizes the internal prior of the specific DEM as well as the external prior of the DEM dataset and achieves better super-resolution reconstruction results in the experimental results. The results of super-resolution reconstruction by the Bicubic method, Super-Resolution Convolutional Neural Networks (SRCNN), very deep convolutional networks (VDSR), ”Zero-Shot” Super-Resolution networks (ZSSR) and the new method in this paper were compared, and the average RMSE of the super-resolution reconstruction results of the five methods were 8.48 m, 8.30 m, 8.09 m, 7.02 m and 6.65 m, respectively. The mean elevation error at the same resolution is 21.6% better than that of the Bicubic method, 19.9% better than that of the SRCNN, 17.8% better than that of the VDSR method, and 5.3% better than that of the ZSSR method.

Review of Super-Resolution Image Reconstruction Algorithms

Review of Super-Resolution Image Reconstruction Algorithms

Image super-resolution reconstruction algorithm based on significant network connection-collaborative migration structure

Although deep learning is widely used in the field of image super-resolution reconstruction, the number of network layers and the complexity of deep learning models continue to increase, and the speed at which the difficulty of model training increases has exceeded the speed of network performance improvement. This situation prevents deep learning frameworks from fully utilizing their generalization ability in image super-resolution reconstruction. Moreover, the existing image reconstruction methods have the problem of partial information loss. Therefore, this paper first proposes a structure that simulates the feature extraction function in the visual attention mechanism in the convolutional neural network. We call this a significant network connection. The feature information extracted by the network architecture is more significant, while other information is less significant. This setup has a reduced impact on the resulting image reconstruction effect. Then, a network architecture focusing on collaborative information migration is proposed. This architecture can obtain the implicit domain of the intermediate state of the image domain to be reconstructed, and it can make the two networks use the learned hidden domain during the reconstruction process. The dual networks trained in this way are more symmetrical. This approach can better maintain the common feature information of the reconstructed image and effectively solve the problem of partial feature information loss in the image. The experimental results show that the texture, artificial effects and noise of the reconstructed image obtained by the method proposed in this paper are significantly improved over those of the images produced by other mainstream methods. In addition, the method proposed in this paper exhibits a certain degree of improvement over other deep learning methods in terms of model training speed and feature information retention.

Recognition and Detection of Wide Field Bionic Compound Eye Target Based on Cloud Service Network.

In this paper, a multidisciplinary cross-fusion of bionics, robotics, computer vision, and cloud service networks was used as a research platform to study wide-field bionic compound eye target recognition and detection from multiple perspectives. The current research status of wide-field bionic compound-eye target recognition and detection was analyzed, and improvement directions were proposed. The surface microlens array arrangement was designed, and the spaced surface bionic compound eye design principle cloud service network model was established for the adopted spaced-type circumferential hierarchical microlens array arrangement. In order to realize the target localization of the compound eye system, the content of each step of the localization scheme was discussed in detail. The distribution of virtual spherical targets was designed by using the subdivision of the positive icosahedron to ensure the uniformity of the targets. The spot image was pre-processed to achieve spot segmentation. The energy symmetry-based spot center localization algorithm was explored and its localization effect was verified. A suitable spatial interpolation method was selected to establish the mapping relationship between target angle and spot coordinates. An experimental platform of wide-field bionic compound eye target recognition and detection system was acquired. A super-resolution reconstruction algorithm combining pixel rearrangement and an improved iterative inverse projection method was used for image processing. The model was trained and evaluated in terms of detection accuracy, leakage rate, time overhead, and other evaluation indexes, and the test results showed that the cloud service network-based wide-field bionic compound eye target recognition and detection performs well in terms of detection accuracy and leakage rate. Compared with the traditional algorithm, the correct rate of the algorithm was increased by 21.72%. Through the research of this paper, the wide-field bionic compound eye target recognition and detection and cloud service network were organically provide more technical support for the design of wide-field bionic compound eye target recognition and detection system.