Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

Sun, Guanqun; Zhang, Fangzheng

doi:10.1109/access.2020.3004860

Cited by 12 publications

(6 citation statements)

References 26 publications

(27 reference statements)

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“…where n is the stream (1, 2), l is the layer, x is the input and Hid is the hidden layer (Equation ( 4)). CNN-based Fastback projection is the denoising approach that reduces the BP noise from the low-resolution image (Sun & Zhang, 2020). Mishra et al propose (Mishra et al, 2020) The proposed method has differed from the usual feature extraction and processing mechanism.…”

Section: Related Workmentioning

confidence: 99%

Multi‐scaled feature fusion enabled convolutional neural network for predicting fibrous dysplasia bone disorder

Arirangan

Kottursamy

2021

Expert Systems

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Background: Convolutional Neural Network (CNN) has exciting advantages in the processing of medical images. It produces the denoised and segmented results of images. When an image has orientation and scaling issues over the bundle that is not correctly recognized by the classifier. Methods: To overcome these issues, we propose Extreme Deep Learning on Feature Fusion Convolutional Neural Network (EDL-FFCNN) for classification. Feature fusion workflow enhances the ability to learn the feature from the inconsistent behaviour. And, to learn the texture, colour, and intensities of an image. The initial step of the FFCNN framework is to reconstruct the images using filtered back projection. It reduces the noise in edges and cross-sectional areas. Contrast Limited Adaptive Histogram Equalization and Local Binary Pattern is applied to an image to adjust and extract the intensity and textural features of an image. The proposed FFCNN is a decision-based approach that consists of two persistent classifiers. One, the convolutional features are combined with manually extracted multi-scaled features. Subsequently, the normalization techniques are built-in every layer to adjust the input dimensions. It also helps to reduce the training time. As an outcome of Image abnormalities is considered as knowledge transfer to the CNN. Results: The fused features on the CNN classifier produce the abnormality prediction results. Second, the Inception V2 CNN classifier framework classifies the bone images with extracted features. With these two comparative-classification results, the major counting has been taken as the decision for final abnormality prediction.The proposed model gives improved accuracy of 95% for the elbow, 93% for the forearm, 92% for the shoulder, 92% for the wrist, 93% for the finger, 92% for humerus images.

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Section: Related Workmentioning

confidence: 99%

Multi‐scaled feature fusion enabled convolutional neural network for predicting fibrous dysplasia bone disorder

Arirangan

Kottursamy

2021

Expert Systems

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“…Based on these range profiles, the amplitudes at each pixel of the imaging area can be obtained through interpolation. The obtained coarse images of different azimuth time are coherently accumulated layer by layer to get the final image [8 ]. Since BP algorithm only considers the delay information, it avoids the approximation in (3 ) and does not have the problem of migration correction.…”

Section: Isar Imaging With Bp Algorithmmentioning

confidence: 99%

“…The imaging time for BP algorithm is measured to be 5.364 s, indicating the imaging speed is much slower than that of RD algorithm. To enhance the imaging speed, we use the FBP method as proposed in [8 ]. The imaging result is shown in Fig.…”

Section: Isar Imaging With Bp Algorithmmentioning

confidence: 99%

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Frequency‐domain versus time‐domain imaging for photonics‐based broadband radar

et al. 2020

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Photonics‐based radar enables a large operation bandwidth that is particularly favourable for high‐resolution synthetic aperture imaging. However, how to implement fast and high‐accuracy imaging with photonics‐based broadband radar is still an open question. In this Letter, the performance of photonics‐based inverse synthetic aperture radar imaging with frequency‐domain and time‐domain algorithms are experimentally investigated and compared. The results show that, frequency‐domain range–Doppler algorithm has fast imaging capability, but the image suffers from defocusing and distortions. Although migration compensation techniques can be applied to improve the imaging accuracy, the imaging speed is significantly reduced. On the other hand, using time‐domain back‐projection (BP) algorithm can achieve well‐focused images, and the imaging speed can be enhanced with fast BP methods. Therefore, the time‐domain imaging method is found to be the best choice for fast and high‐accuracy synthetic aperture imaging with photonics‐based broadband radars.

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“…The convolutional neural network (CNN) has achieved great success in various computer vision tasks. (4) The semantic segmentation task (5) system in self-driving consideration is essential for recognizing object detection and classification images. (6) The traditional image segmentation mainly involves feature extraction and classification.…”

Section: Introductionmentioning

confidence: 99%

Semantic Image Segmentation in Similar Fusion Background for Self-driving Vehicles

Wu¹,

Tai²,

Lai³

2022

Sensors and Materials

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Self-driving vehicles have become increasingly popular in recent years. Because of this, the information fusion sensing method using radar and cameras has been widely adopted in vehicles. We use the vehicle camera sensor and robust image segmentation technology to solve its inherent shortcomings. The images used for image segmentation are obtained under adverse weather conditions, or the image object's color and texture resemble the background. For such images, using the convolutional layer model for image segmentation as a feature extraction method usually leads to error. Any highly robust algorithms for image enhancement for self-driving operation will help alleviate problems related to driving safety. To ensure that the final image segmentation achieves the desired effect and reduces the error rate, we propose a new segmentation-twice method, which correctly classifies the object's label. The test results of the simulation described in this paper show that this experiment correctly classifies the object's label. It can provide accurate environmental perception information for autonomous vehicles, improve the segmentation effect of similar fusion background images, and reduce the error rate.

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Convolutional Neural Network (CNN)-Based Fast Back Projection Imaging With Noise-Resistant Capability

Cited by 12 publications

References 26 publications

Multi‐scaled feature fusion enabled convolutional neural network for predicting fibrous dysplasia bone disorder

Multi‐scaled feature fusion enabled convolutional neural network for predicting fibrous dysplasia bone disorder

Frequency‐domain versus time‐domain imaging for photonics‐based broadband radar

Semantic Image Segmentation in Similar Fusion Background for Self-driving Vehicles

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