Zobrazeno 1 - 10
of 9 380
pro vyhledávání: '"Dictionary Learning"'
Autor:
Apeksha Pandey, Manepalli Sai Teja, Parul Sahare, Vipin Kamble, Mayur Parate, Mohammad Farukh Hashmi
Publikováno v:
Journal of Electrical Systems and Information Technology, Vol 11, Iss 1, Pp 1-23 (2024)
Abstract Skin conditions are becoming increasingly prevalent across the world in current times. With the rise in dermatological disorders, there is a need for computerized techniques that are completely noninvasive to patients’ skin. As a result, d
Externí odkaz:
https://doaj.org/article/d050d11f0bd4424190ac072f4fc16601
Autor:
Bhawna Goyal, Ayush Dogra, Ammar Jalamneh, Dawa Chyophel Lepcha, Ahmed Alkhayyat, Rajesh Singh, Manob Jyoti Saikia
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Images captured in low-light environments are severely degraded due to insufficient light, which causes the performance decline of both commercial and consumer devices. One of the major challenges lies in how to balance the image enhancement
Externí odkaz:
https://doaj.org/article/1c399be01c0f4310b8290b267cbba44a
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
The shallow sea underwater acoustic channel exhibits a significant sparse multipath structure. The temporally multiple sparse Bayesian learning (TMSBL) algorithm can effectively estimate this sparse multipath channel. However, the complexity of the a
Externí odkaz:
https://doaj.org/article/5a0104f185ff41c18ee15e3690751abc
Publikováno v:
IEEE Open Journal of Intelligent Transportation Systems, Vol 5, Pp 393-408 (2024)
From the perspective of artificial intelligence evaluation, the need to discover and explain the potential shortness of the evaluated intelligent algorithms/systems as well as the need to evaluate the intelligence level of such testees are of equal i
Externí odkaz:
https://doaj.org/article/148ea933199a41a9970d4e1bb09f28f0
Publikováno v:
IEEE Access, Vol 12, Pp 90418-90431 (2024)
Compressed sensing theory is widely used to accurately reconstruct the original signal from a small number of random observations, i.e., obtain high-dimensional information from low-dimensional information. This feature has shown effectiveness in ima
Externí odkaz:
https://doaj.org/article/031637c6e94f44ef95232beda1d07021
Autor:
JiLan Huang, ZhiXiong Jin
Publikováno v:
Tehnički Vjesnik, Vol 31, Iss 3, Pp 774-783 (2024)
Image denoising is essential for numerous image processing applications, where image noise can profoundly impact processing efficiency and output quality. Addressing the challenge of inflexible reference images in unconditional diffusion probability
Externí odkaz:
https://doaj.org/article/280f24d2937f46cfb5e6bf4504fc3347
Publikováno v:
IEEE Access, Vol 12, Pp 35957-35971 (2024)
This paper presents a robust subject-wise sequential dictionary learning (swsDL) algorithm named rswsDL for functional magnetic resonance imaging (fMRI) data where the negative impact of dimensionality reduction in the form of information loss and se
Externí odkaz:
https://doaj.org/article/b708846ad84f4e5eb61ba56693f173d9
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 17, Pp 5221-5233 (2024)
Denoising plays a fundamental role in ground penetrating radar (GPR) data processing and determines the effect of anomaly extraction, inversion imaging, and other subsequent processing. In recent years, the sparse dictionary representation method k-s
Externí odkaz:
https://doaj.org/article/9774a635ffee47a7be10b564f28f41b3
Autor:
Usman Haider, Muhammad Hanif, Ahmar Rashid, Khursheed Aurangzeb, Akhtar Khalil, Musaed Alhussein
Publikováno v:
IEEE Access, Vol 12, Pp 5837-5850 (2024)
In histopathological image analysis, the feature extraction task for classification proves to be demanding. This difficulty arises from the assortment of histological features appropriate for individual problems and the intricate presence of diverse
Externí odkaz:
https://doaj.org/article/8645da9c001140aaa367879d89763bb8
Publikováno v:
IEEE Open Journal of Signal Processing, Vol 5, Pp 168-176 (2024)
We propose sparse representation and dictionary learning algorithms for dictionaries whose atoms are characterized by Gaussian probability distributions around some central atoms. This extension of the space covered by the atoms permits a better char
Externí odkaz:
https://doaj.org/article/4dbf3f67a53a444ab61e0062a30f8a1c