A deep learning approach to image quality assessment
Deep convolutional neural networks (DCNNs) have an unchallengeable performance advantage over traditional machine learning in solving visual problems. However, DCNNs are vulnerable when the input signals are distorted or manipulated maliciously. We explore the computational modeling of image quality...
Saved in:
Main Author: | Feng, Yeli |
---|---|
Other Authors: | Cai Yiyu |
Format: | Theses and Dissertations |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/85493 http://hdl.handle.net/10220/50465 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Using deep learning for quality control in cyber-manufacturing
by: Dai, Wenting
Published: (2022) -
Fine-grained image classification using deep learning
by: Sun, Deguang
Published: (2022) -
Deep learning approach for detection of melanoma from skin lesion images
by: Ku, Hui Sien
Published: (2019) -
Photorealistic stylised image quality assessment database (PSIQAD) building and modelling
by: Low, Qing Ru
Published: (2020) -
Image quality assessment based label smoothing in deep neural network learning
by: Chen, Zhou
Published: (2018)