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Author Topic: Best downsize to reduce noise?  (Read 9077 times)
madmanchan
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« Reply #20 on: January 11, 2012, 02:10:21 PM »
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My earlier example about producing an image with only 1 pixel was partly a joke, but also partly serious.  We know that such an image has no noise.  The original image may have a lot of noise.  As you transition from the original image to the smallest possible image (1 pixel), you transition from the original amount of noise to zero noise.  In other words, noise will go down.  The exact nature of that transition will depend a lot on the resampling method.
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hjulenissen
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« Reply #21 on: January 11, 2012, 02:22:51 PM »
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My earlier example about producing an image with only 1 pixel was partly a joke, but also partly serious.  We know that such an image has no noise.
"Noise" corresponds more or less measurement error, right? Or the non-linear, non-deterministic part of the error or whatever (I am sure Joofa have something to say on that).

If you took 2 shots of the same scene and averaged both down to single pixel size, chances are that those two single pixel images would differ ever so slightly. I vote that this difference is indication that both images contains some noise (granted only Dc-component).

I think that some light can be shed on this topic by studying Wiener filters. Basically a Wiener filter is the solution to the problem "what linear, time (or space)-invariant filter will minimize the residual error after filtering a signal corrupted by noise"

http://www.dspguide.com/ch17/3.htm

If total SNR was the only thing that affected our perception, and LTI filters was the only tool that we had at our disposal to improve it, and signal/noise characteristics were known, I believe that Wiener filters would be the solution to all noise reduction problems. Clearly, this is not the case.

-h
« Last Edit: January 11, 2012, 02:40:26 PM by hjulenissen » Logged
theguywitha645d
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« Reply #22 on: January 11, 2012, 02:45:41 PM »
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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. 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.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


As you said, it is an extreme example. I am not claiming I know the answer, but down sampling does not eliminate noise from my image as noise is random--not having a cycle like your example--and you still need to preserve the signal--what is the difference of an image of a sandy beach and a noisy sky? And what is the difference of viewing two images that are 300dpi+ with one down sampled and the other at native resolution, do we perceive the images as the same? So does the inability to resolve smaller pixels have the same visual effect of down sampling?

Perhaps it would help to know more details from the OP and what tests the OP has done on this.
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bjanes
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« Reply #23 on: January 11, 2012, 03:05:17 PM »
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... but down sampling does not eliminate noise from my image as noise is random--not having a cycle like your example--and you still need to preserve the signal--what is the difference of an image of a sandy beach and a noisy sky? And what is the difference of viewing two images that are 300dpi+ with one down sampled and the other at native resolution, do we perceive the images as the same? So does the inability to resolve smaller pixels have the same visual effect of down sampling?

Noise is random, but downsampling can reduce the randomness and thus the noise. Consider two flat frames taken with the Nikon D3 at ISO 3200. The standard deviation is the noise. If I measure the standard deviation of a 400 x 400 pixel area and compare it to the standard deviation of a 200 x 200 pixel downsize using bicubic in Photoshop, the standard deviation (the noise) decreases. This is confirmed by visual inspection of the images.

Regards,

Bill
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theguywitha645d
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« Reply #24 on: January 11, 2012, 05:11:40 PM »
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Bill, here is may question. If I have two 16x20 prints viewed at 20 inches and from the same image, but one was from the original file and one was down sampled, would I see a difference in noise? Would the visual system do the basic averaging work of down sampling?
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madmanchan
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« Reply #25 on: January 11, 2012, 05:30:54 PM »
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I think of noise as variance, or standard deviation, or some statistical measure of variation in image values (take your pick).  The 1-pixel image has zero variation.  Hence zero noise.

Same thing happens in the real world when you view something with texture (like a rock or brick wall) and then move away from it.  Eventually you will not see the texture anymore and you'll just see a flat tone.
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bjanes
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« Reply #26 on: January 11, 2012, 06:00:29 PM »
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Bill, here is may question. If I have two 16x20 prints viewed at 20 inches and from the same image, but one was from the original file and one was down sampled, would I see a difference in noise? Would the visual system do the basic averaging work of down sampling?

That is an interesting question, and it would depend in part on the visual acuity of the observer and on the characteristics of the printing process. Error diffusion printing as with an inkjet softens the image somewhat as compared to a continuous tone device, and some of the noise might be smoothed out by the printing process with an inkjet. However, if you are making a 16 by 20 print, you would likely want all the resolution that you can get (even with an IQ180) and it would not make sense to downsize merely to reduce noise. A dedicated NR program such as Noiseware would be a better choice if noise were an problem. Why downsize to reduce noise and lose resolution, only to do upsizing (either in Photoshop or the printer driver) when the printing?

Image normalization for resolution (as done by DXO) makes more sense when comparing two sensors with differing resolution. If you have resolution to spare with the higher resolution sensor when making a given print size, downsizing would be necessary either in Photoshop or the printer driver and this would improve the signal to noise ratio. Whether or not the difference would be noticeable in a print depends on many variables.

Regards,

Bill
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hjulenissen
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« Reply #27 on: January 11, 2012, 11:47:22 PM »
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I think of noise as variance, or standard deviation, or some statistical measure of variation in image values (take your pick).  The 1-pixel image has zero variation.  Hence zero noise.
So what is the cause of slight differences between two single-pixel images taken under identical conditions?

-h
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Fine_Art
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« Reply #28 on: January 12, 2012, 01:10:37 AM »
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So what is the cause of slight differences between two single-pixel images taken under identical conditions?

-h

Have you tried the experiment with some old images downsampling to 1 pixel? Probably moving clouds, maybe some from sensor temp.
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Fine_Art
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« Reply #29 on: January 12, 2012, 01:17:08 AM »
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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.


Edmund

 

Run noise software to remove the noise wiping out some fine detail, then downsample to hide the mush. Ive done this with a 75% downsample ratio. I had no way to know if there was an optimum ratio. I assumed the wavelets left intact would fit in 75% pixels. Yes, it works.
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hjulenissen
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« Reply #30 on: January 12, 2012, 01:45:22 AM »
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Bill, here is may question. If I have two 16x20 prints viewed at 20 inches and from the same image, but one was from the original file and one was down sampled, would I see a difference in noise? Would the visual system do the basic averaging work of down sampling?
It is good scientific practice to try to isolate variables. If you scale your image at some point in the pipeline, but keep the absolute print size constant, your printer or some other component will scale the image up to compensate. Knowing how it does that and predicting the quality of the end-result is hard.

My suggestion is to experiment with different lowpass filters (the primary noise-altering component in a scaler is a lowpass filter) while keeping the pixel grid constant.

-h
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Fine_Art
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« Reply #31 on: January 12, 2012, 02:48:10 AM »
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Original from one of the software vendor's websites vs NR then downsampled.
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hjulenissen
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« Reply #32 on: January 12, 2012, 05:53:49 AM »
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Original from one of the software vendor's websites vs NR then downsampled.
The second one looks less noisy to me, hard to conclude as they are rendered at different sizes on my display... What am I supposed to conclude?

-h
« Last Edit: January 12, 2012, 06:05:29 AM by hjulenissen » Logged
BartvanderWolf
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« Reply #33 on: January 12, 2012, 06:45:34 AM »
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Noise is random, but downsampling can reduce the randomness and thus the noise. Consider two flat frames taken with the Nikon D3 at ISO 3200. The standard deviation is the noise. If I measure the standard deviation of a 400 x 400 pixel area and compare it to the standard deviation of a 200 x 200 pixel downsize using bicubic in Photoshop, the standard deviation (the noise) decreases. This is confirmed by visual inspection of the images.

Hi Bill,

Indeed, downsampling will (by weighted averaging) cancel some of the highest frequency noise. But the important thing is that also the high spatial frequency (HSF) signal is reduced, often in quite a similar amount (so there is no real improvement). Of course the noise which is mostly random will be reduced and there will usually be some remaining signal at the new resolution, so there is some increase in S/N ratio at the expense of loss of detail.

Therefore the question becomes, do we measure image quality as lower noise but also having lost HSF signal, or as high Signal to Noise (with HSF detail to spare). I would favor the latter, which can be achieved with a dedicated noise reduction application quite effectively with only minimal reduction of HF signal. This keeps the larger output option intact, which is important to many.

If the OP's goal is to also reduce file size, and not only by lossless compression, then reducing noise before downsampling wil provide superior results. In the absense of noise the down-sampling filter becomes very important if aliasing artifacts are to be avoided.

Cheers,
Bart
« Last Edit: January 12, 2012, 08:44:21 AM by BartvanderWolf » Logged
theguywitha645d
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« Reply #34 on: January 12, 2012, 09:57:13 AM »
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It is good scientific practice to try to isolate variables. If you scale your image at some point in the pipeline, but keep the absolute print size constant, your printer or some other component will scale the image up to compensate. Knowing how it does that and predicting the quality of the end-result is hard.

My suggestion is to experiment with different lowpass filters (the primary noise-altering component in a scaler is a lowpass filter) while keeping the pixel grid constant.

-h

Well, I can just do it on my printer, but as you pointed out, there are systemic factors. But my question was a little more fundamental. Assuming a losses system, given the same image, one down sampled and one native, when actually viewed at the same size and same distance, would the human visual system perceive the difference, after all, none of the pixels in each image can be resolved and some sort of averaging would be done in situ so to speak.
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eronald
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« Reply #35 on: January 12, 2012, 06:15:18 PM »
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If the OP's goal is to also reduce file size, and not only by lossless compression, then reducing noise before downsampling wil provide superior results. In the absense of noise the down-sampling filter becomes very important if aliasing artifacts are to be avoided.

Cheers,
Bart

Bart,

 The OP's goal is to obtain a nice file that is very substantially downsized, eg to 1/4 the pixels.

Edmund
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Edmund Ronald, Ph.D. 
BJL
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« Reply #36 on: January 12, 2012, 07:01:27 PM »
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What is the significance of 6MP target?

Is it because some use case forbids files of higher pixel count, like some web site with that is its maximum allowed pixel count?

Is it because you believe that this will give better IQ at high ISO speeds than, for example, applying NR but keeping the full pixel count, or just printing at the size you intend to use with the 6MP but at twice the PPI?

It is hard to judge alternative approaches without knowing the objective. (I mean the goal, not the lens.)

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Fine_Art
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« Reply #37 on: January 12, 2012, 07:08:14 PM »
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The second one looks less noisy to me, hard to conclude as they are rendered at different sizes on my display... What am I supposed to conclude?

-h
Conclude what you will, the images have to tell the tale.

Like I said the first one is the poster shot at a noise reduction software website. The second shot has noise removed by me in a competing software. It is downsampled to look like it has detail at the pixel level.

Which looks better?
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eronald
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« Reply #38 on: January 12, 2012, 07:22:11 PM »
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What is the significance of 6MP target?

Is it because some use case forbids files of higher pixel count, like some web site with that is its maximum allowed pixel count?

Is it because you believe that this will give better IQ at high ISO speeds than, for example, applying NR but keeping the full pixel count, or just printing at the size you intend to use with the 6MP but at twice the PPI?

It is hard to judge alternative approaches without knowing the objective. (I mean the goal, not the lens.)



I am interested in the case where I simply don't need 24MP; Assume, I want 6MP, or even 3MP because I am going into a BADLY LIT locale with an absolute NECESSITY to print a single magazine page or get a good web shot; that I need every bit of shutter speed and DOF I can squeeze out of the body and so I'm stuck with 6400 ISO. Now what is the best workflow to postprocess the 14 bit 6400 ISO Raw image into a colorful, sharp, small, destination file?

Edmund
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Edmund Ronald, Ph.D. 
hjulenissen
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« Reply #39 on: January 13, 2012, 12:05:49 AM »
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Conclude what you will, the images have to tell the tale.
Images can be selected that aids generalized conclusions, and ones can be selected that does not aid conclusions. Comparing two images on my display of different sizes surely looks visibly different, but so does an image of a horse compared to an image of a cat...

My point being that you should resize image#2 so that it matches image#1. Then one can form sensible conclusions about which is "better". I could do this for myself, but my scaling might not be the same as yours, and then it would be difficult to exchange opinions.

-h
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