Shakib-IO/Diminishing_Image_Noise_Using_Deep_Learning
Denoising an image is a classical problem that researchers are trying to solve for decades. In earlier times, researchers used filters to reduce the noise in the images. They used to work fairly well for images with a reasonable level of noise. However, applying those filters would add a blur to the image. And if the image is too noisy, then the resultant image would be so blurry that most of the critical details in the image are lost. There has to be a better way to solve this problem. As a result, I have implemented several deep learning architectures that far surpass the traditional denoising filters. In this blog, I will explain my approach step-by-step as a case study, starting from the problem formulation to implementing the state-of-the-art deep learning models, and then finally see the results.
This project helps anyone working with images by removing unwanted visual 'noise' that degrades their quality. It takes a blurry or grainy image as input and outputs a cleaner, clearer version without the blur often caused by traditional methods. This is for photographers, imaging scientists, or anyone who needs high-quality images from noisy sources.
No commits in the last 6 months.
Use this if you need to restore clarity to noisy images and traditional filtering methods leave your images blurry or still contain too much noise.
Not ideal if your image noise is minimal or if you prefer simpler, faster traditional filters that don't require deep learning expertise.
Stars
11
Forks
2
Language
Jupyter Notebook
License
—
Category
Last pushed
Dec 27, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/Shakib-IO/Diminishing_Image_Noise_Using_Deep_Learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
CAREamics/careamics
A deep-learning library for denoising images using Noise2Void and friends (CARE, PN2V, HDN...
yu4u/noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration...
rgeirhos/texture-vs-shape
Pre-trained models, data, code & materials from the paper "ImageNet-trained CNNs are biased...
NICALab/SUPPORT
Accurate denoising of voltage imaging data through statistically unbiased prediction, Nature Methods.
jaewon-lee-b/lte
Local Texture Estimator for Implicit Representation Function, in CVPR 2022