Digikam/SoK2012/AutoNR: Difference between revisions
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digiKam already has a good noise reduction algorithm but it is with manual NR settings. What we need is a Automatic NR settings adjustment option depending upon the input image's noise level and the type of noise present. This would automate the task and will also make it easier for newbies to find the NR settings which 'just works' for the input image. | digiKam already has a good noise reduction algorithm but it is with manual NR settings. What we need is a Automatic NR settings adjustment option depending upon the input image's noise level and the type of noise present. This would automate the task and will also make it easier for newbies to find the NR settings which 'just works' for the input image. | ||
http://farm9.staticflickr.com/8452/7998679054_5d334b51cd_d.jpg | |||
Noise level and its type in a image may depend upon various factors such as Camera, ISO and other conditions. A statistical variance between neighboring pixel may give us a rough estimate about the salt & pepper noises. Moreover, this project needs to tested it over a large set of data inorder to get a good relation between detected parameters and settings of wavelet NR. | Noise level and its type in a image may depend upon various factors such as Camera, ISO and other conditions. A statistical variance between neighboring pixel may give us a rough estimate about the salt & pepper noises. Moreover, this project needs to tested it over a large set of data inorder to get a good relation between detected parameters and settings of wavelet NR. | ||
Revision as of 04:49, 18 September 2012
digiKam SoK 2012 Auto Noise Reduction
digiKam already has a good noise reduction algorithm but it is with manual NR settings. What we need is a Automatic NR settings adjustment option depending upon the input image's noise level and the type of noise present. This would automate the task and will also make it easier for newbies to find the NR settings which 'just works' for the input image.
Noise level and its type in a image may depend upon various factors such as Camera, ISO and other conditions. A statistical variance between neighboring pixel may give us a rough estimate about the salt & pepper noises. Moreover, this project needs to tested it over a large set of data inorder to get a good relation between detected parameters and settings of wavelet NR.
A simple workflow : from an Input Image we will detect noise level and type of noise; use other info available like camera model, ISO, etc; and to complete, compute Auto Adjust wavelets NR settings.
One of the primary goal is to implement a Noise Level Function (NFL) which works using using a single image[1]. Here in the image data is fit in each region with a smoothing function, then noise is estimated according to the residue remaining. This may over estimate some images which naturally have frequent changes in color, textures, lighting, etc.
Secondly we need to map the obtained Noise Level to the already present NR settings (This would certainly require much of testing with the real image dataset).
Project Timeline
Updates
Todo
| Milestone name | Milestone description | Assigned to | Expected Start Date | Status |
|---|---|---|---|---|
| NLF | Implement the function for Noise estimation as in [1] | Aniket | 1 June |
Reference and Links
[1] Noise Estimation from a Single Image : Richard Szeliski; Sing Bing Kang; Ce Liu William; T. Freeman, 2006
[2] Fast method for noise level estimation and integrated noise reduction: Bruna, A.; Messina, G.; Spampinato, G., 2005
Bugzilla entry for this project
Note