# Deep Joint Demosaicking and Denoising SiGGRAPH Asia 2016
Michaël Gharbi gharbi@mit.edu Gaurav Chaurasia Sylvain Paris Frédo Durand
A minimal pytorch implementation of “Deep Joint Demosaicking and Denoising” [Gharbi2016]
# Installation
From this repo:
`shell python setup.py install `
Using pip:
`shell pip install demosaicnet `
Then run the demo script with:
`shell python scripts/demosaicnet_demo.py output `
To train a dummy model on the demo dataset provided, run:
`shell python scripts/train.py --data demosaicnet/data/dummy_dataset --checkpoint_dir ckpt `
To build and update the whee:
`shell pip install wheel twine make distribution make upload_distribution `
# FAQ
How is noise handled? Where is the pretrained model? The noise-aware model is not implementation, see the earlier Caffe implementation for that <https://github.com/mgharbi/demosaicnet_caffe>
How do I train this? The script scripts/train.py is a good start to setup your training job, but I haven’t tested it yet, I recommend rolling your own.
1 maintainer
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file demosaicnet-0.0.14.macosx-12.4-arm64.tar.gz.
File metadata
- Download URL: demosaicnet-0.0.14.macosx-12.4-arm64.tar.gz
- Upload date:
- Size: 3.7 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.2 CPython/3.9.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
f7a8126c0366b84acdfab1f8487a7aceeeada7f8926547a6053740546041b095
|
|
| MD5 |
ec6b728c8f59e50f06a0bc7577a21484
|
|
| BLAKE2b-256 |
4c0da3f8ddbc2ddd0ff2ae18987c695c26b1658a45237812504d520cbad925d3
|
File details
Details for the file demosaicnet-0.0.14-py3-none-any.whl.
File metadata
- Download URL: demosaicnet-0.0.14-py3-none-any.whl
- Upload date:
- Size: 3.6 MB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
twine/4.0.2 CPython/3.9.1
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
e50d502a1336736fc83fdaca97c6e709c3114419083cb1854def3355fb6894c9
|
|
| MD5 |
9dc4b56881feb01725ce99e23f0559a4
|
|
| BLAKE2b-256 |
1e87048de313cecc62268dbd7c5d9f35ea34887e84330bb4d8d03610c63eef5b
|
