Low-Light Image Enhancement Using Image-to-Frequency Filter Learning

Sobbahi, Rayan Al; Tekli, Joe

doi:10.1007/978-3-031-06430-2_58

Cited by 5 publications

(1 citation statement)

References 28 publications

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“…Remote sensing images can clearly express the internal geometric structures and rich spatial information of objects (Yuan et al, 2021;Yu et al, 2018;Kemker et al, 2018;Al-Sobbahi & Tekli, 2022). The resolutions of remote sensing images have become higher and higher, the data dimensions (space, time, spectra, etc.)…”

Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

Zhang,

Jiang

2023

International Journal on Semantic Web and Information Systems

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There are many problems with remote sensing images, such as large data scales, complex illumination conditions, occlusion, and dense targets. The existing semantic segmentation methods for remote sensing images are not accurate enough for small and irregular target segmentation results, and the edge extraction results are poor. The authors propose a remote sensing image segmentation method based on a DCNN and multiscale feature fusion. Firstly, an end-to-end remote sensing image segmentation model using complete residual connection and multiscale feature fusion was designed based on a deep convolutional encoder–decoder network. Secondly, weighted high-level features were obtained using an attention mechanism, which better preserved the edges, texture, and other information of remote sensing images. The experimental results on ISPRS Potsdam and Urban Drone datasets show that compared with the comparison methods, this method has better segmentation effect on small and irregular objects and achieves the best segmentation performance while ensuring the computation speed.

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Section: Introductionmentioning

confidence: 99%

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

Zhang,

Jiang

2023

International Journal on Semantic Web and Information Systems

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A new dynamic DNA-coding model for gray-scale image encryption

Afify

Sharkawy

Gad

et al. 2023

Complex Intell. Syst.

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To protect the images and provide a more secure cipher image, DNA encoding is crucial in image encryption. Applying a single, easily detectable coding rule to the image during DNA encoding has no impact on the encryption model's security level. Therefore, using various coding rules while applying encryption to the image, dynamic DNA-coding techniques have emerged to strengthen and improve the encryption of the image and its security. This study integrates a dynamic DNA-coding method with an encryption model. The model is applied to gray-scale images, where using a predetermined coding rule, every two bits are DNA-encoded in the image. The proposed model generates the key by sending the image and its metadata to hash functions. Following that, the hyperchaotic system constructs three chaotic sequences using the key, and the Lorenz–Liu chaotic system generates a sequence of coding rules. Then, the image is passed to Arnold Transform, where the resulted image is diffused by applying five chaotic maps. Last, using the coding rules, it is DNA-encoded, provided with the chaotic sequences to DNA, and DNA-decoded. Twelve metrics were used to assess the proposed model on ten widely used images. Results show a promising improvement in performance, since it enhanced the security of the model.

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A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

Ramalingam,

Selvi,

Victor

et al. 2023

IEEE Access

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Blockchain (BC) and Computer Vision (CV) are the two emerging fields with the potential to transform various sectors. BC can offer decentralized and secure data storage, while CV allows machines to learn and understand visual data. The integration of the two technologies holds massive promise for developing innovative applications that can provide solutions to the challenges in various sectors such as supply chain management, healthcare, smart cities, and defense. This review explores a comprehensive analysis of the integration of BC and CV by examining their combination and potential applications. It also provides a detailed analysis of the fundamental concepts of both technologies, highlighting their strengths and limitations. This paper also explores current research efforts that make use of the benefits offered by this combination. The BC can be used as an added layer of security in CV systems and also ensure data integrity, enabling decentralized image and video analytics. The challenges and open issues associated with this integration are also identified, and appropriate potential future directions are also proposed.

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Low-Light Image Enhancement Using Image-to-Frequency Filter Learning

Cited by 5 publications

References 28 publications

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

Remote Sensing Image Semantic Segmentation Method Based on a Deep Convolutional Neural Network and Multiscale Feature Fusion

A new dynamic DNA-coding model for gray-scale image encryption

A Comprehensive Analysis of Blockchain Applications for Securing Computer Vision Systems

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