zwx8981/DBCNN
Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Network
This project helps evaluate the quality of images without needing a perfect reference version. You provide an image, and it assesses how much it has been degraded by common distortions like blur, noise, or compression. This is useful for anyone working with digital images who needs to automatically check or compare their visual integrity, such as photographers, content creators, or quality control specialists.
117 stars. No commits in the last 6 months.
Use this if you need to objectively measure the quality of digital images that might have been distorted, without having an original, pristine version for comparison.
Not ideal if you primarily work with video quality assessment or if you prefer a Python-based solution, as this implementation is in MATLAB.
Stars
117
Forks
21
Language
MATLAB
License
MIT
Category
Last pushed
Apr 01, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zwx8981/DBCNN"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
clovaai/synthtiger
Official Implementation of SynthTIGER (Synthetic Text Image Generator), ICDAR 2021
ocampor/image-quality
Image quality is an open source software library for Image Quality Assessment (IQA).
idealo/image-quality-assessment
Convolutional Neural Networks to predict the aesthetic and technical quality of images.
hzwer/ICCV2019-LearningToPaint
ICCV2019 - Learning to Paint With Model-based Deep Reinforcement Learning
onuralpszr/GFPGAN-ncnn-vulkan
[WIP] NCNN with Vulkan implementation of GFPGAN aims at developing Practical Algorithms for...