Ad
Ad
Ad
Pages: [1] 2 3 4 »   Bottom of Page
Print
Author Topic: Best downsize to reduce noise?  (Read 9163 times)
eronald
Sr. Member
****
Offline Offline

Posts: 3645



WWW
« on: January 10, 2012, 02:52:19 PM »
ReplyReply

I have a D3x. Nice camera. But I'd like to be able to get results like from a D3 or D3s Smiley
In other words, I would like to be able to get good 6MP or so images at high ISO.
Discuss.

Edmund
Logged

Edmund Ronald, Ph.D. 
MichaelEzra
Sr. Member
****
Offline Offline

Posts: 642



WWW
« Reply #1 on: January 10, 2012, 03:35:28 PM »
ReplyReply

The interpolation method used for preview rendering in RawTherapee seems to remove all noise on smaller zoom scales. This is available in preview only, however.
Logged

eronald
Sr. Member
****
Offline Offline

Posts: 3645



WWW
« Reply #2 on: January 10, 2012, 03:42:57 PM »
ReplyReply

There should be some interesting stuff one could do here - maybe not enuff to turn a D3x into a D3s, but at leats enuff to not make it worthwhile to buy a D3s Smiley

Edmund

The interpolation method used for preview rendering in RawTherapee seems to remove all noise on smaller zoom scales. This is available in preview only, however.
Logged

Edmund Ronald, Ph.D. 
BartvanderWolf
Sr. Member
****
Offline Offline

Posts: 3019


« Reply #3 on: January 10, 2012, 06:15:51 PM »
ReplyReply

I have a D3x. Nice camera. But I'd like to be able to get results like from a D3 or D3s Smiley
In other words, I would like to be able to get good 6MP or so images at high ISO.
Discuss.

Hi Edmund,

It depends on the spectral noise profile whether downsampling really helps (you will lose resolution when downsampling). The best approach that always works is to use a dedicated noise reduction application first (and then downsample if needed).

The noise spectrum can be estimated by using e.g. ImageJ and a (radial profile) Plug-in. It's ideally based on the result of 2 subtracted images of a uniformly lit featureless surface, but one can also follow the simple empirical method of trial and error.

Cheers,
Bart
Logged
ejmartin
Sr. Member
****
Offline Offline

Posts: 575


« Reply #4 on: January 10, 2012, 06:42:22 PM »
ReplyReply

Bart has it right.  Straight downsampling (the preview in RT is a simple pixel binning operation) doesn't use any of the image information on scales finer than the target resolution.  A dedicated NR program will use that information and thereby lead to a better result (sharper, less noise) after downsampling than simply blurring (to remove fine scale noise and suppress aliasing) followed by downsampling, or downsampling without blurring.
Logged

emil
theguywitha645d
Sr. Member
****
Offline Offline

Posts: 970


« Reply #5 on: January 10, 2012, 06:50:31 PM »
ReplyReply

My understanding is downsizing is just masking the noise through binning, but since you are really just reducing artifacts by making bigger pixels and the pixels themselves are still unresolved by the viewer, I don't really know if you are really doing anything to the final perceived image. If noise is like granularity (you never perceive grain, just the effects of it), then two prints at the same dimensions may not actually look any different.
Logged
eronald
Sr. Member
****
Offline Offline

Posts: 3645



WWW
« Reply #6 on: January 10, 2012, 07:11:27 PM »
ReplyReply

Hi Edmund,

It depends on the spectral noise profile whether downsampling really helps (you will lose resolution when downsampling). The best approach that always works is to use a dedicated noise reduction application first (and then downsample if needed).

The noise spectrum can be estimated by using e.g. ImageJ and a (radial profile) Plug-in. It's ideally based on the result of 2 subtracted images of a uniformly lit featureless surface, but one can also follow the simple empirical method of trial and error.

Cheers,
Bart

Bart,

 Please give references.

EJMartin,
 
  The "dedicated" noise reduction algorithm I am looking for is one which "knows" that I am satisfied with a picture with 1/4 of the number of pixels in the end.

  And BTW, surely such things should be done on the Raw, and not on the debayered tonemapped Tiff?

Edmund
Logged

Edmund Ronald, Ph.D. 
madmanchan
Sr. Member
****
Offline Offline

Posts: 2100


« Reply #7 on: January 10, 2012, 07:53:52 PM »
ReplyReply

Hi Edmund,  you can downsize your image to 1 pixel.  Guaranteed to have no noise.   Grin
Logged

ejmartin
Sr. Member
****
Offline Offline

Posts: 575


« Reply #8 on: January 10, 2012, 09:33:30 PM »
ReplyReply

  And BTW, surely such things should be done on the Raw, and not on the debayered tonemapped Tiff?

One has to be sure not to obliterate the correlations among the color channels that assist in demosaicing the resulting image.  Without that, denoising the color channels separately prior to demosaic will typically yield a worse result.
Logged

emil
ErikKaffehr
Sr. Member
****
Offline Offline

Posts: 6925


WWW
« Reply #9 on: January 10, 2012, 11:08:48 PM »
ReplyReply

Hi,

What I don't understand really is why the D3S has less noise than the D3X when D3X is downscaled to same size. It seems that D3S has less shot noise which may indicate that the high ISO performance is achieved due to better quantum efficiency? Would be interesting to find out.

Regarding noise reduction and downscaling it's my impression that it would be best done in raw conversion. Noise reduction in LR3 works pretty well.
What used to be Bibble Pro could integrate Noise Ninja at early stage.

Best regards
Erik

I have a D3x. Nice camera. But I'd like to be able to get results like from a D3 or D3s Smiley
In other words, I would like to be able to get good 6MP or so images at high ISO.
Discuss.

Edmund
Logged

EricWHiss
Sr. Member
****
Offline Offline

Posts: 2307



WWW
« Reply #10 on: January 10, 2012, 11:19:54 PM »
ReplyReply

Edmund,
I'm just doing this with LR3 or C1 with NR set higher than I'd like for full size and then outputting at 25% with decent results.  In general I dislike to use Luminance NR because it seems to muddle as much as it does clean, but this magically goes away at 25%.   What kind of noise is the problem? Shadows probably but pattern or splotches or purple green stuff? 
Logged

Authorized Rolleiflex Dealer:
Find product information, download user manuals, or purchase online - Rolleiflex USA
Fine_Art
Sr. Member
****
Offline Offline

Posts: 890


« Reply #11 on: January 11, 2012, 12:06:56 AM »
ReplyReply

Hi,

What I don't understand really is why the D3S has less noise than the D3X when D3X is downscaled to same size. It seems that D3S has less shot noise which may indicate that the high ISO performance is achieved due to better quantum efficiency? Would be interesting to find out.

Regarding noise reduction and downscaling it's my impression that it would be best done in raw conversion. Noise reduction in LR3 works pretty well.
What used to be Bibble Pro could integrate Noise Ninja at early stage.

Best regards
Erik


If the electronics around the pixel are a required thickness for electrical reasons smaller pixels will have a higher percentage of lost light gathering area. This would not be the case with Canon's claimed 100% coverage microlenses or Sony's Exmor with the electronics under the pixel.

Yes, Bibble's integrated NN at raw conversion was effective. They really hyped it as an important advantage. Too bad Bibble's Colors started to suck on newer cameras. It was awesome with the A100.

Regarding OP question,
Noise is an overused parameter perhaps due to it being easy to measure. If noise is removed with a competent software a higher resolution image almost always looks more realistic than a lower one. The only reason people dont always see that is the 2MP screens we use. Images at 50% always look much better than 100% due to oversampling. Same deal.
Logged
hjulenissen
Sr. Member
****
Offline Offline

Posts: 1615


« Reply #12 on: January 11, 2012, 01:16:17 AM »
ReplyReply

Re: downsampling reduces noice! - uh noise!
Re: downsampling reduces noice! - uh noise! - con't.

Quote
Here is a crop from a plenty-noisy Imaging Resource D3s 102400 ISO shot. It has been processed as follows:

1 Original

2 Lowpass filtered, everything above Nyquist/2 zeroed using DFT. Not downsampled.

3 Original downsampled by factor 2, without filtering. Equivalent to "Nearest-neighbor" or true "decimation by 2." Includes aliases due to lack of lowpass filter.

4 Lowpass filtered as in 2, then downsampled as in 3. "Properly downsampled."

5 Image 4 subtracted from image 3 to reveal noisy aliases resulting from lack of lowpass filter. (offset 128 has been added)


My take:
If you are to downsample a noisy image anyways, you should select a scaling algorithm that does plenty of averaging (smoothing everything above fs/2).

I dont see the point in downsampling to remove noise, use your favorite noise reduction algorithm instead.

-h
« Last Edit: January 11, 2012, 01:18:25 AM by hjulenissen » Logged
eronald
Sr. Member
****
Offline Offline

Posts: 3645



WWW
« Reply #13 on: January 11, 2012, 05:06:22 AM »
ReplyReply

Look, I'm a scientist -or was- but at this point I am trying to be purposely vague.

For me, the point of the exercise, as I see it, is to start with the Raw images of a pretty good camera with lots of pixels (24MP), and determine the best way to process those Raws in order to gain say 1-2 stops of subjective ISO, shooting at  6400 instead of 1600, and end up with enough pixels for a nice rather than ugly magazine page.

A typical application would be if taking the camera to a fashion show, and hitting conditions that force one to use a higher shutter speed and DOF eg. because it is desirable to shoot the model as she is walking and not posed at runway end. The D3x landed me with that dilemma more than once.

More prosaically, when I pick up my huge SLR to take an image of my toddler playing with his mum in the very dimly lit living room, I am quite certain that I won't need 24MP, but I still want that fullframe look, or I'd pick up some other camera Smiley

Pros who face low-light action situations usually have a dedicated camera, but for those who do not, it may be interesting to know how a studio camera can cope. This may be interesting to the MF crowd too because an 80MP camera has a lot of room for downsizing Wink

Edmund

 
« Last Edit: January 11, 2012, 05:15:06 AM by eronald » Logged

Edmund Ronald, Ph.D. 
hjulenissen
Sr. Member
****
Offline Offline

Posts: 1615


« Reply #14 on: January 11, 2012, 06:25:41 AM »
ReplyReply

Look, I'm a scientist -or was- but at this point I am trying to be purposely vague.

For me, the point of the exercise, as I see it, is to start with the Raw images of a pretty good camera with lots of pixels (24MP), and determine the best way to process those Raws in order to gain say 1-2 stops of subjective ISO, shooting at  6400 instead of 1600, and end up with enough pixels for a nice rather than ugly magazine page.
In other words: you want to reduce the visible noise, and are willing to sacrifice details/sharpness to get there? Isn't that noise-reduction in a nutshell?

-h
Logged
mediumcool
Sr. Member
****
Offline Offline

Posts: 672



« Reply #15 on: January 11, 2012, 07:49:48 AM »
ReplyReply

The options possible in Photoshop scaling can help, if further software purchases are not wanted.
Logged

FaceBook facebook.com/ian.goss.39   www.mlkshk.com/user/mediumcool
bjanes
Sr. Member
****
Offline Offline

Posts: 2718



« Reply #16 on: January 11, 2012, 08:07:01 AM »
ReplyReply

What I don't understand really is why the D3S has less noise than the D3X when D3X is downscaled to same size. It seems that D3S has less shot noise which may indicate that the high ISO performance is achieved due to better quantum efficiency? Would be interesting to find out.

Erik,

One reason why a larger pixel performs better than smaller pixels producing a higher resolution image which is then downsized is the difference between hardware and software binning. Consider 4:1 binning of a monochrome image in hardware. The SNR due to shot noise is improved by a factor of two. However, the binned superpixel would be read out with the same read noise as the unbinned pixels. With software binning, 4 read noises would be binned.

With color sensors, hardware binning is considerably more complicated. See this post on the Phase One site.

Regards,

Bill
Logged
theguywitha645d
Sr. Member
****
Offline Offline

Posts: 970


« Reply #17 on: January 11, 2012, 08:16:24 AM »
ReplyReply

Downsampling in and of itself, does not address noise. I don't think noise has simply a pixel level effect where averaging neighboring pixel luminance values takes care of the problem (won't Bayer interpolation do the same?)--noisy images still look noisy at different magnifications other than 100%. So noise is like waves on a ocean, at different scales, you are still able to perceive noise in the frame just like you can still see the surface of the water is not flat.

Why do you think this would work and what have you been trying?
Logged
bjanes
Sr. Member
****
Offline Offline

Posts: 2718



« Reply #18 on: January 11, 2012, 01:48:57 PM »
ReplyReply

Downsampling in and of itself, does not address noise. I don't think noise has simply a pixel level effect where averaging neighboring pixel luminance values takes care of the problem (won't Bayer interpolation do the same?)--noisy images still look noisy at different magnifications other than 100%. So noise is like waves on a ocean, at different scales, you are still able to perceive noise in the frame just like you can still see the surface of the water is not flat.

Why do you think this would work and what have you been trying?

Downsizing does affect noise, and that is the basis for the normalization that DXO does to compare a higher resolution sensor to a lower resolution one. See the explanation by DXO here. Those familiar with statistics will recognize that this is the same as the calculation of standard error. By collecting more photons, the sampled mean of a picture element will be a better representation of the true mean. A similar principle is with polling (as with the recent elections). To get a more accurate statistic, the pollster takes a larger sample size.

Regards,

Bill

Logged
hjulenissen
Sr. Member
****
Offline Offline

Posts: 1615


« Reply #19 on: January 11, 2012, 02:02:35 PM »
ReplyReply

Downsampling in and of itself, does not address noise.
Reasonable downsampling includes a lowpass filter. A lowpass filter reduce the energy of any signal/noise within the stopband. SNR tends to be poor at high spatial frequencies.
Quote
I don't think noise has simply a pixel level effect where averaging neighboring pixel luminance values takes care of the problem (won't Bayer interpolation do the same?)-
High-quality Bayer reconstruction usually tries to keep sharp edges (guess image information that is unknown), meaning that it tends to be vulnerable to sensor noise (even amplify it) unless great care is taken.
Quote
-noisy images still look noisy at different magnifications other than 100%. So noise is like waves on a ocean, at different scales, you are still able to perceive noise in the frame just like you can still see the surface of the water is not flat.

Why do you think this would work and what have you been trying?
I like extreme examples. I am not saying that this is what happens, but I hope that this will make you think through your claims.

Imagine that a line of pixels should have had luminance values of:
[17 17 17 17 17 17 17 17]
(signal has no high-frequency components, only DC)

Then imagine that the pixels have been read out with an error ("noise") turning it into:
[16 18 16 18 16 18 16 18]
(noise has only a single frequency component, and seems to be deterministic)

What do you think averaging would do?

In the real world, signal and noise are not perfectly separated in the frequency domain, and it is extremely hard to change one without affecting the other. It seems that practically usable algorithms exist, though, where some (small) loss of signal quality is accepted for a significant reduction in perceived noise.

-hj
« Last Edit: January 11, 2012, 02:07:01 PM by hjulenissen » Logged
Pages: [1] 2 3 4 »   Top of Page
Print
Jump to:  

Ad
Ad
Ad