Deep dilated CNN based image denoising

Chaurasiya, Rashmi; Ganotra, Dinesh

doi:10.1007/s41870-022-01125-2

Cited by 14 publications

(3 citation statements)

References 37 publications

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“…Deep learning is a cutting-edge scientific domain that aims to capitalize on emerging scenarios by providing fresh solutions and applications. "Deep" denotes the numerous layers involved in the data transformation process [21], [22]. To elaborate, it is a specialized variant of machine learning that grasps the representation of the actual world as intricate hierarchies of concepts, where simpler and more abstract concepts and representations delineate each concept.…”

Section: Deep Learning (Cnn Classifier) Of 2d Model and 3d Modelmentioning

confidence: 99%

Revolutionization of augmented reality in tourism via deep learning

Essa,

Chaturvedi,

Khurana

2024

IJEECS

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Tourism has become an integral part of social and economic development across the globe. It does not only serve as a recreational activity but also as a source of revenue for the nation. The paper systematically explores the potential enhancements in the tourist experience through cutting-edge technology. Employing deep learning methods, the study specifically concentrates on refining augmented reality encounters for visitors. The proposed approach utilizes deep learning algorithms to optimize and tailor tourists’ augmented reality experiences, addressing current sectoral challenges like customization and engagement shortcomings. The methodology’s selection is predicated on it is capability to elevate user experience, accurately identify objects, offer visual guided tours, integrate historical context, and ultimately propel augmented reality adoption in tourism. Notably, the investigation culminates in a noteworthy average accuracy of 99% when incorporating deep learning to enhance augmented reality in tourism.

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Section: Deep Learning (Cnn Classifier) Of 2d Model and 3d Modelmentioning

confidence: 99%

Revolutionization of augmented reality in tourism via deep learning

Essa,

Chaturvedi,

Khurana

2024

IJEECS

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“…Despeckling performance can be further improved if the number of kernels, pyramid levels, and recursive blocks increases. Extensive experiments were conducted to evaluate the performance, examining various factors such as the number of feature maps (16,32,64), pyramid levels (3,5,7), and recursive blocks (3,5,7). Although there is a minor improvement in PSNR and SSIM values, visual image quality remains the same.…”

Section: Parameter Settingsmentioning

confidence: 99%

“…CNN is also adopted for image-denoising tasks, and attention-guided denoising convolutional neural network (ADNET) [6] is a commonly used algorithm. Although CNN-based approaches [7] perform exceptionally well, a few limitations hinder their application in various low and high-level vision tasks. Firstly, most CNN-based methods used single-scale decomposition or single-stream CNN structure for training [8].…”

Section: Introductionmentioning

confidence: 99%

PAN-DeSpeck: A Lightweight Pyramid and Attention-Based Network for SAR Image Despeckling

Yasmeen,

Usman Yaseen,

Sohaib Ali

et al. 2023

Computers, Materials &Amp; Continua

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SAR images commonly suffer from speckle noise, posing a significant challenge in their analysis and interpretation. Existing convolutional neural network (CNN) based despeckling methods have shown great performance in removing speckle noise. However, these CNN-based methods have a few limitations. They do not decouple complex background information in a multi-resolution manner. Moreover, they have deep network structures that may result in many parameters, limiting their applicability to mobile devices. Furthermore, extracting key speckle information in the presence of complex background is also a major problem with SAR. The proposed study addresses these limitations by introducing a lightweight pyramid and attention-based despeckling (PAN-Despeck) network. The primary objective is to enhance image quality and enable improved information interpretation, particularly on mobile devices and scenarios involving complex backgrounds. The PAN-Despeck network leverages domainspecific knowledge and integrates Gaussian Laplacian image pyramid decomposition for multi-resolution image analysis. By utilizing this approach, complex background information can be effectively decoupled, leading to enhanced despeckling performance. Furthermore, the attention mechanism selectively focuses on key speckle features and facilitates complex background removal. The network incorporates recursive and residual blocks to ensure computational efficiency and accelerate training speed, making it lightweight while maintaining high performance. Through comprehensive evaluations, it is demonstrated that PAN-Despeck outperforms existing image restoration methods. With an impressive average peak signal-to-noise ratio (PSNR) of 28.355114 and a remarkable structural similarity index (SSIM) of 0.905467, it demonstrates exceptional performance in effectively reducing speckle noise in SAR images. The source code for the PAN-DeSpeck network is available on GitHub.

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