Results 51 to 60 of about 24,230 (259)

INDEX QUALITY ASSESMENT CITRA TERINTERPOLASI (SSIM dan FSIM)

JUTEI (Jurnal Terapan Teknologi Informasi), 2017
Ada sejumlah aplikasi dalam pengenalan pola yang membutuhkan citra dengan ukuran tertentu. Ukuran citra menentukan hasil dari pengenalan pola suatu sistem. Suatu metode interpolasi digunakan untuk menyesuaikan ukuran suatu citra.
Meirista Wulandari
doaj   +1 more source

A novel image augmentation based on statistical shape and intensity models: application to the segmentation of hip bones from CT images

European Radiology Experimental, 2023
Background The collection and annotation of medical images are hindered by data scarcity, privacy, and ethical reasons or limited resources, negatively affecting deep learning approaches.
Jérôme Schmid, Lazhari Assassi, Christophe Chênes   +2 more
doaj   +1 more source

MSE, PSNR, SSIM, RMSE and DSC of 10 images.

, 2022
MSE, PSNR, SSIM, RMSE and DSC of 10 images.
Sidratul Montaha (13200394), Sayma Islam (13200400), Mirjam Jonkman (9765452), A. K. M. Rakibul Haque Rafid (13200397), Sami Azam (9765449), Pronab Ghosh (13200403)   +5 more
core   +1 more source

Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets Through Dynamic Dense Scattering Media

Advanced Science, EarlyView.
Vision begins not with images but with change: the retina fires only when something moves, handing the brain a stream of spikes. A neuromorphic imaging system adopts the same strategy—an event sensor that sees like the retina, a spiking network that thinks like the brain—to track and reconstruct fully randomly moving targets through tissue (phantom ...
Ning Zhang, Arto Nurmikko
wiley   +1 more source

Autoencoder–Based Image Representation Learning with Kolmogorov–Arnold Networks

International Journal of Applied Mathematics and Computer Science
Autoencoders are widely used for learning compact latent representations of images. While convolutional architectures dominate this area due to their ability to exploit spatial locality, recent developments in neural network design have introduced ...
Urbaniak Ilona Anna, Wieczorek Sylwester, Kołodziej Joanna   +2 more
doaj   +1 more source

Deep‐Learning‐Based Denoising for Improved Phase Precision in Electron Holography of Electromagnetic Fields in Nanoscale Materials

Advanced Science, EarlyView.
Low‐dose electron holography is limited by shot noise, which buries weak phase signals. HoloDenoiser, a physics‐informed network that works simultaneously in the spatial and frequency domains, locates and protects the holographic sideband while suppressing noise in the hologram.
Ye Luo, Luyang Wang, Zhiling Yu, Xiaowen Chen, Xiaoyu Wang, Ya‐Hui Jia, Thibaud Denneulin, Yu Han, Rafal E. Dunin‐Borkowski, Fengshan Zheng, Qi Liu   +10 more
wiley   +1 more source

GAN-TL: Generative Adversarial Networks with Transfer Learning for MRI Reconstruction

Applied Sciences, 2022
Generative adversarial networks (GAN), which are fueled by deep learning, are an efficient technique for image reconstruction using under-sampled MR data.
Muhammad Yaqub, Feng Jinchao, Shahzad Ahmed, Kaleem Arshid, Muhammad Atif Bilal, Muhammad Pervez Akhter, Muhammad Sultan Zia   +6 more
doaj   +1 more source

SSIM-based non-local means image denoising

, 2011
Perceptually inspired image processing has been an emerging field of study in recent years. Here we make one of the first efforts to incorporate the structural similarity (SSIM) index, a successful perceptual image quality assessment measure, into the ...
Zhou Wang, Abdul Rehman
core   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

SSIM values of 20 random images of MIT-5K.

, 2022
SSIM values of 20 random images of MIT-5K.
Lihua Bi (2265031), Canlin Li (12700223), Jinjuan Zhu (12700226), Weizheng Zhang (6006350), Yan Liu (25061)   +4 more
core   +1 more source