Results 41 to 50 of about 65,529 (267)

Video quality assessment method: MD-SSIM [PDF]

2015 IEEE International Conference on Consumer Electronics - Taiwan, 2015
In this paper, video quality assessment (VQA) for compression losses is the main focus. A new method, MD-SSIM (Mean Squared Error Difference SSIM) is used for detecting the spatial distortion. For the temporal part, the differences of the SSIM scores of each frame are used to form the quality scores.
Woei-Tan Loh, David B. L. Bong
openaire   +1 more source

nOMP: A New Sparse Solution to Enhance the SSIM Levels of OMP-Based Encoded Images

IEEE Access, 2022
This work comprises the development of a quality enhancement technique for image encoders that use compressive sensing. The recommended solution seeks to maximize the perceptual quality based objective function, unlike other sparse representation ...
A. N. Omara, Hesham Farouk, Sayed A. Mohamed   +2 more
doaj   +1 more source

Learning to Generate Images with Perceptual Similarity Metrics

, 2017
Deep networks are increasingly being applied to problems involving image synthesis, e.g., generating images from textual descriptions and reconstructing an input image from a compact representation.
Liao, Renjie, Mozer, Michael C., Ridgeway, Karl, Roads, Brett D., Snell, Jake, Zemel, Richard S.   +5 more
core   +1 more source

Signature Verification System Using SSIM In Image Processing

Journal of Scientific Research and Technology
The verification of signatures is an essential function in several domains, including financial, legal, and administrative processes. Thanks to advancements in image processing, automatic signature verification methods have become more popular.
Dr. Megha Rani Raigonda, Shweta
semanticscholar   +1 more source

SSIM—A Deep Learning Approach for Recovering Missing Time Series Sensor Data

IEEE Internet of Things Journal, 2019
Missing data are unavoidable in wireless sensor networks, due to issues such as network communication outage, sensor maintenance or failure, etc. Although a plethora of methods have been proposed for imputing sensor data, limitations still exist.
YiFan Zhang, P. Thorburn, W. Xiang, P. Fitch   +3 more
semanticscholar   +1 more source

Application of convolutional neural networks trained on optical images for object detection in radar images

Компьютерная оптика
Due to the small number of annotated radar image datasets, the use of optical images for training neural networks designed to detect objects in radar images seems promising.
V.A. Pavlov, A.A. Belov, S.V. Volvenko, A.V. Rashich   +3 more
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

Improved SR-SSIM Band Selection Method Based on Band Subspace Partition

Remote Sensing, 2023
Scholars have performed much research on reducing the redundancy of hyperspectral data. As a measure of the similarity between hyperspectral bands, structural similarity is used in band selection methods.
Tingrui Hu, Peichao Gao, Sijing Ye, Shi Shen   +3 more
doaj   +1 more source

Image Deblurring and Super-resolution by Adaptive Sparse Domain Selection and Adaptive Regularization

, 2010
As a powerful statistical image modeling technique, sparse representation has been successfully used in various image restoration applications. The success of sparse representation owes to the development of l1-norm optimization techniques, and the fact ...
Dong, Weisheng, Shi, Guangming, Wu, Xiaolin, Zhang, Lei   +3 more
core   +1 more source

End‐to‐End Sensing Systems for Breast Cancer: From Wearables for Early Detection to Lab‐Based Diagnosis Chips

Advanced Materials Technologies, EarlyView.
This review explores advances in wearable and lab‐on‐chip technologies for breast cancer detection. Covering tactile, thermal, ultrasound, microwave, electrical impedance tomography, electrochemical, microelectromechanical, and optical systems, it highlights innovations in flexible electronics, nanomaterials, and machine learning.
Neshika Wijewardhane, Lyn I. Jones, Richard M. Martin, Helen Winter, Faezeh Arab Hassani   +4 more
wiley   +1 more source