### Show Posts

This section allows you to view all posts made by this member. Note that you can only see posts made in areas you currently have access to.

### Messages - Juan Conejero

Pages: [1] 2 3 ... 259
1
##### Release Information / Comparison of noise reduction algorithms: The double gradient synthetic image
« on: 2013 May 17 20:10:43 »
Hi all,

Since we are discussing on noise reduction, let me put a more formal example to compare all the noise reduction algorithms that we have implemented in PixInsight. This time I'm going to use a well-known difficult problem: the double gradient synthetic image.

The double gradient consists of two concentric squares filled with opposite linear gradients. It can be generated very easily in PixInsight with the following PixelMath expression, executed globally to generate a new image, or locally on an image of the desired dimensions:

iif( X() < 0.25 || X() > 0.75 || Y() < 0.25 || Y() > 0.75, X(), 1 - 2*(X() - 0.25) )

When synthetic noise is added to this image, the result is an ill-posed problem for noise reduction, and is particularly well suited to show the weaknesses---and comparatively, the strong points---of different noise reduction algorithms. Here is the double gradient with a 25% of uniform noise added with the NoiseGenerator tool in PixInsight. This is the initial image that I have used in this test:

I have tried all the noise reduction tools available on the PixInsight platform, trying to do my best by fine tuning parameters to achieve the best possible result in each case. Here are the denoised images, enlarged 2:1 without interpolation:

TGVDenoise

ACDNR

MultiscaleMedianTransform

GREYCstoration

ATrousWaveletTransform

And here is my interpretation of these results:

* TGVDenoise is the absolute winner. It has been able to recover the original gradients almost perfectly with minimal generation of artifacts. Its superior result is clear and admits no discussion.

* ACDNR has been a nice surprise for me. I designed and implemented the ACDNR algorithm back in 2005. I know its weak points very well (it has many), but honestly, this result has been kind like a good old friend shouting "hey, I'm still here!" in my face

* The multiscale median transform (MMT) is a powerful tool for denoising and ringing-free sharpening of linear and nonlinear images, as we have demonstrated many times (see for example here and here), but it has two main limitations: Our implementation of MMT is not particularly good at reproducing sharp corners because it uses circular structuring elements to preserve isotropy, and median filters are not very good at reproducing smooth gradients. The next generation of MMT that we'll implement during the 1.8 cycle combines the à trous wavelet transform for smooth regions and the MMT for significant structures, taking the best from each algorithm, so it will pass this test much better.

* The GREYCstoration algorithm yields a decent result, but it generates significant artifacts around the edges of the inner square (also at the right border of the image), and does not remove the noise on smooth regions as efficiently as the preceding tools. I spent a long time trying to improve this result, but this is the best one I was able to achieve. Perhaps somebody with more practice (I admit I don't use this tool very often) would be able to get something better.

* The à trous (with holes) wavelet transform, also known as starlet transform recently, is a fundamental processing workhorse of great efficiency where isotropy and smoothness are two characteristic properties of the data, as happens with most deep-sky astronomical images. The inability of ATWT to isolate strong small-scale structures---something that MMT can do much better---becomes evident in the double gradient problem: When enough wavelet coefficients are removed to yield a smooth result, the edges of the inner square cannot be preserved because they penetrate the whole transform as a gun shot.

I have uploaded a PixInsight project that you can use to reproduce these results. If you manage to improve what I have done with some of the algorithms tested here, please let me know and post your parameters here.

2
##### Release Information / Re: TGVDenoise Example: Gamma Cygni Region
« on: 2013 May 17 11:07:32 »
Gorgeous image. Sorry, I couldn't resist

(disclaimer: I haven't paid attention to the stars, so the brightest ones are slightly concave)

It's nice to see how the same data can lead to different interpretations, different ways to understand the properties and limitations of the image with good analysis and processing tools, but always respecting the data. The people that paint their images (you know, brushes, pencils, 'selective sharpening' by hand and all that stuff) really don't know how much are those practices blocking their creativity.

3
##### Release Information / Re: TGVDenoise Example: Gamma Cygni Region
« on: 2013 May 17 10:02:29 »
Hi Philippe,

For noise reduction of linear images, the STF is critical. In your tests, you've used a much more aggressive STF than the one I used in my example. It's not that your STF is incorrect, by no means; it's just that it reveals much more noise in low-SNR areas of the image, as a result of a stronger nonlinear stretch.

I have tried to emulate the STF you've used, and have repeated my example using it:

The image shown above represents a heavy nonlinear stretch that would require some dynamic range compression (IMO) with the HDRMultiscaleTransform tool.

On this screenshot you can also see the TGVDenoise parameters that I've used in this second example. This time I've enabled the local support, using the target image and the same midtones balance and shadows clipping point values that I have set for STF. In other words, the local support is just what you see on the screen.

Also note that I have decreased the smoothness parameter to 0.3. This has allowed me to control the degree of smoothness on low-SNR regions.

As for the number of iterations, beyond 200 iterations or so the results don't change visibly with these parameters, so the process converges quickly.

I think the results are very nice with this configuration. Here are three comparisons zoomed 2:1 without interpolation.

In my opinion, all of the nebular structures that can be seen on the denoised images over dark regions are real, in the sense that they have been recorded in your data set. The noise reduction process just makes them more visible, which is a very nice result. You can verify this visually by comparing the before and after crops.

As for mixing the original and denoised images with a mask, it's clearly an option, but I think it isn't necessary with advanced noise reduction algorithms such as MultiscaleMedianTransform and TGVDenoise. A drawback of reinserting some of the original noise in the denoised image is that it creates staircase artifacts, i.e. relatively hard transitions at the edges of low-SNR structures. Personally, I lost the need for comfort noise long time ago (that happens when you design noise reduction tools ), and definitely prefer smooth gradients.

4
##### Release Information / TGVDenoise Example: Gamma Cygni Region
« on: 2013 May 16 18:26:46 »
Philippe Bernhard has kindly uploaded a set of six linear images for us to test TGVDenoise out. In this first example I'm going to show how easily we can achieve a very good noise reduction when the data are excellent.

The original image is an integration of 20 light frames of the Gamma Cygni region:

The six images that Philippe has uploaded are of excellent quality, as you'll see in the next screenshots. This is the linear output of the ImageIntegration process, so we need ScreenTransferFunction (STF) in order to see the image on the screen. For newbie users, the STF tool in PixInsight applies a histogram transformation to the data that are sent to the screen, but does not change the actual image. This way we can work with linear images without stretching them. This is very important because data linearity is necessary for some essential image processing algorithms and techniques, such as color calibration, background modeling and deconvolution. There are also some tools that can be applied to both linear and nonlinear images, but perform much better with linear data.

For noise reduction of linear images, it is very important to carefully adjust STF settings, so that we can see the background, and at the same time, bright areas without saturation. Usually the automatic STF stretch is too aggressive for this purpose. The next screenshot shows the original image enlarged 4:1 with the custom STF that I've used in this example.

And without more preambles, this is the same area after TGVDenoise:

As you probably have already discovered if you have tried out this tool, the default TGVDenoise parameters have been optimized for nonlinear images. For linear images, the default parameters are way too aggressive, and usually have to be lowered by one order of magnitude or even more.

Something that will cause some surprise is the fact that I haven't used a local support in this example. This will give you a better idea of the power of TGVDenoise: For images with relatively high SNR, this tool is able to remove virtually all of the noise, preserving significant structures remarkably well without any help from external data.

Once you get some experience with the tool, finding the correct parameters becomes very easy, especially for high-quality data. It has taken me about five minutes to achieve the noise reduction that I was looking for with this image. I'll show you the result on two additional previews for comparison.

Original image:

After TGVDenoise:

Original image:

After TGVDenoise:

And as usual, some crops of special interest zoomed 3:1 (left: original image, right: after TGVDenoise).

Now this image can be printed at a size of 1 meter by 80 cm at 300 dpi without problems (about 40 x 30 inches), and navigating through it onscreen zoomed 4x is a real joy.

Thank you Philippe for allowing me to work with your nice data.

5
##### Release Information / Re: TGVDenoise Example: Noise Reduction in CIE Lab Mode
« on: 2013 May 16 17:06:28 »
Thank you so much to all who have uploaded their images: Philippe, Geert, Don and Harry.

I have just completed a first example with one of Philippe's images, and will be working with the rest during the next days. These images will be of great help for us to understand and further develop the TGVDenoise tool.

6
##### New Scripts and Modules / Re: Annotation script
« on: 2013 May 16 10:02:44 »
Quote
Will Juan distribute it via the update mechanism?

Sure thing. Can I proceed with the zip file you have posted here, Andrés?

7
##### Announcements / Re: PixInsight 1.8.0 RC7 "Ripley" Released
« on: 2013 May 16 09:58:49 »
Hi,

FreeBSD installation packages for PixInsight 1.8.0.1017 RC7 are now available on the software distribution site. Please note that we have only tested this version on FreeBSD 9.1. Older releases than 9.0 are no longer supported officially.

Enjoy!

8
##### Release Information / Re: TGVDenoise Example: Noise Reduction in CIE Lab Mode
« on: 2013 May 15 10:45:20 »
Hi Philippe,

Thanks. Yes, this tool requires a lot of practice. Well, actually not more than any complex tool. Once you get some practice you start 'feeling' the parameters and it becomes easier each time you process a new image.

What we need now is more data to test the tool. If you can upload an image, I'll be glad to make an example and post it here.

9
##### General / Re: How do I transfer a saved project to another PC and open it.
« on: 2013 May 15 08:27:15 »
Hi Mike,

A PixInsight project consists of a .xosm file and a .data folder. For example, if you save a "MyBestImageEver" project, you'll get the following two items on the folder where you saved the project:

MyBestImageEver.xosm
MyBestImageEver.data

The .data folder is where all the project data are stored:  images, icons, processing histories, tool settings, text files, etc. The .xosm file describes your project, all the associated data and their relations.

XOSM stands for XML Object Serialization Module, and is PixInsight's project format. The XOSM format is fully relocatable. This means that you can move or copy a project to any location on your filesystem, or move it to another machine, and it will continue working without problems.

This is true for internal project data, such as open images. However, if your project contains 'hardwired' absolute file paths---for example, ImageContainer icons, or file lists in tools such as ImageIntegration, StarAlignment, BatchPreprocessing, etc---, PixInsight has no way to know where these files are (in case they still exist) if you copy or move your project to a different machine, so the items that depend on such files won't work and fail with 'no such file' messages.

There are several ways to prevent and/or fix these problems. The best way is keeping all of your PixInsight projects and data on external storage devices, which you can mount at the same point on different machines. For example, if you use Windows, you can assign the same drive letter to an external disk on two or more PCs. On UNIX and Linux, removable devices can be mounted at arbitrary locations on the filesystem, which allows for more flexible configurations.

If the above is not possible for some reason, you can always edit the XOSM file to replace nonexistent absolute file paths with valid paths. Since XOSM is plain text, it can be edited with any good code editor, such as PixInsight's Script Editor.

10
##### Release Information / Re: TGVDenoise Example: Noise Reduction in CIE Lab Mode
« on: 2013 May 14 20:28:21 »
Thank you Geert,

Quote
in order to get the final result,  did you first apply tgv on ligness and afterwards on crominance or did you apply it all together at the final step?

In the final step I have enabled both lightness and chrominance parameters. You could also apply first to lightness only, then to chrominance only. This can be useful, for example, to use a different support image at each stage.

Quote
I did a little test on the tool last night, and got some ringing on stars

That should never happen under normal conditions. If you upload the image in question, I'd be glad to try out TGVDenoise on it. Keep in mind that we are still learning to use this tool!

11
##### Release Information / Re: TGVDenoise Example: Noise Reduction in CIE Lab Mode
« on: 2013 May 14 20:22:57 »
Hi Lex,

Thank you!

Quote
it will work by the same manner on mono or OSC CCD data?

TGVDenoise works with any kind of images, including linear and nonlinear (stretched) images, color and grayscale images.

12
##### Release Information / Re: TGVDenoise Example: Noise Reduction in CIE Lab Mode
« on: 2013 May 14 18:15:34 »
Hi Sander,

Thanks! Yes, we'll gather all the material we are posting here to write an official documentation for TGVDenoise.

13
##### Release Information / TGVDenoise Example: Noise Reduction in CIE Lab Mode
« on: 2013 May 14 18:08:09 »
Hi all,

As promised, here is a first noise reduction example with the new TGVDenoise tool. To show a complete workflow with color data, I have chosen an image that poses a relatively difficult problem for noise reduction. It is a Canon EOS 7D raw image that has already been stretched and saturated with the HistogramTransformation and CurvesTransformation tools in PixInsight.

A strong color saturation curve has been applied directly after the initial stretch, which has been an obvious mistake: A side effect of this uncontrolled color saturation boost is generation of severe noise in the chrominance. We know that color saturation enhancements must be applied with some control (with protection masks, and/or after noise reduction, etc.), in order to prevent chrominance noise intensification. But this is just an example, where I have taken the liberty to make some things difficult on purpose.

The resulting image is an interesting noise reduction problem, mainly due to the following factors:

(1) Severe chrominance noise, for the reasons explained above.

(2) Fine details at scales dominated by the noise.

(3) Low-contrast structures that can be problematic for edge protection.

(4) Extended background areas with strong noise and no significant detail.

Reason (1) is why we have implemented a special CIE Lab working mode in the TGVDenoise tool. When the noise is very different (in its distribution and intensity) for the lightness and chroma components, applying two separate noise reductions seems the most logical option. Reasons (2) and (3), and the fact that they are plainly opposite to (4), suggest the need for a local support image to drive the TGV regularization algorithm.

As usual, we'll work on a small preview covering a region of special interest. Recall that as long as we don't enable its automatic convergence parameter, TGVDenoise is a fully previewable process, so what we'll get on the preview will be consistent with the result obtained on the whole image.

The screenshot above will give you an idea of the severity of the noise in our working image, especially color noise.

Our first step is building a local support image. By default, the TGVDenoise tool uses the intensity component (in the HSI system) of the target image. In this case, however, I prefer to work with the lightness component because it is much less noisy than intensity, due to the color noise problems that I have described. So we'll extract lightness (IMAGE > Extract > Lightness) as a new image and select it as a local support on the TGVDenoise tool.

Recall that a local support is intended to represent the signal-to-noise ratio of the target image, with the purpose of conditioning the TGV noise reduction algorithm. So where the local support image is white we are saying 'this is signal, look but don't touch!', and where the support is black we say 'this is trash, remove it please'. To improve the support in this sense, we have several histogram controls. I have used the midtones balance and shadows clipping parameters to increase contrast, as shown in the next screenshot:

I have also removed the first wavelet layer to make the support slightly less noisy, and hence more accurate. Note that this does not alter the support image in any way; the transformations are applied to an internal working duplicate, never to the selected image.

On the screenshot above, note that I have selected the Preview mode in the Local Support section. In this mode, TGVDenoise replaces the target image with a copy of its internal working support image, which is useful to control the support. We have to disable this mode to perform an actual noise reduction procedure.

As I have said at the beginning of this post, we'll work in the CIE Lab color mode of TGVDenoise in this example. We'll start with lightness noise reduction, so we'll disable chrominance parameters and select the Lightness display mode for the target image. In this way we can concentrate on the lightness, without wasting time processing the chrominance components. This allows us to save a 67% of processing time during our initial testing steps.

After some trial-error work, this is my processed lightness:

Let's repeat the same procedure for the chrominance. Disable lightness parameters and select the "a=R b=G" display mode:

In this mode, the red and green channels show a representation of the CIE a* and CIE b* chrominance components, respectively. This allows you to visualize the color components of the image independently from lightness, that is, what you see on the screen is a special rendition of pure color contents. This mode is especially handy for noise reduction, as you can see in these screenshots. Below is my final processed chrominance.

Note that the chrominance parameters are much more aggressive than the lightness parameters. As you know, the human vision system is rather poor at detecting details defined by color variations. Most of the detail we perceive in an image comes from lightness variations. Thanks to this property of our vision, we usually can apply a comparatively strong noise reduction to the chrominance without damaging the perceived detail. In this example, I have used an edge protection parameter value that is about one order of magnitude larger for chrominance than for lightness.

This is my final denoised image:

For better evaluation of the result, let's take a closer look at some regions of particular interest. Below are three crops of 200x200 pixels enlarged 3:1 without interpolation; original versus processed image.

The result is very good in my opinion, especially considering the severity of the noise in the original image. It is not perfect, of course, and we can easily find positive and negative aspects. For example, some low-contrast structures have been damaged in the green stem of the third crop, and we can find a number of small details that have lost some of their initial color support. On the other hand, low-contrast features have been preserved remarkably well on the shadowed yellow areas of the second crop, and the spider webs have been protected extremely well over a smooth background on the first crop. Overall, this kind of result is very difficult to achieve, if not impossible, with other noise reduction tools that we have implemented in PixInsight. More examples to come.

14
##### Release Information / TGVDenoise 1.0 Released
« on: 2013 May 13 19:00:50 »
Hi everybody,

I am proud to announce that we have just released a new tool: TGVDenoise, written by PTeam member Carlos Milovic. All users have now access to this tool as a regular update to PixInsight 1.8.0 RC7.

TGVDenoise is a noise reduction tool based on total generalized variation (see references [1...5] at the end of this post), a novel signal processing concept with important applications to image regularization and restoration. As I anticipated in a previous post, TGVDenoise is the first one of a series of TGV-based tools that we have been working on during the last year. Other members of this series are a multiscale, spatially adaptive TGV noise reduction tool (SATGV), and an image restoration (deconvolution) tool. Hopefully we'll be releasing these modules during the next weeks and months. I would like to reiterate my congratulations to Carlos Milovic for his excellent research and development work on these tools.

In the rest of this post I'll describe briefly the main TGVDenoise parameters. We'll be posting noise reduction examples on this board during the next days.

Overview of the TGVDenoise tool

The TGVDenoise tool has two separate sections: noise reduction parameters at the top side, and local support parameters at the bottom side. While we write an official documentation, let's describe briefly these sections and their corresponding parameters.

Noise Reduction Parameters

* Color working mode.

For color images, TGVDenoise provides two working modes: RGB/K and CIE L*a*b*. In RGB/K mode, the same set of parameters is applied to each nominal RGB channel, for color images, or to the nominal grayscale channel, for monochrome images. Note that for grayscale images the color working mode is irrelevant.

In CIE L*a*b* mode, the target image is first converted to the CIE L*a*b* color space. Lightness parameters (first tab) are then applied to the CIE L* component, and chrominance parameters (second tab) are applied to the CIE a* and b* components. Finally, the image is converted back to the RGB space. This mode allows you to apply a different (usually more aggressive) noise reduction procedure to the chrominance, which makes the tool more flexible and adaptable to a variety of typical denoising problems. You can disable one of the parameter sets, so you can apply noise reduction just to either the lightness or chroma components.

* Strength

This parameter controls the strength of the diffusion process that smoothens the image. Large values are more aggressive, while small values tend to give results that are more similar to the original image. You have to find a good balance between the strength and edge protection parameters (see below).

* Edge protection

This is by far the most critical parameter of the TGVDenoise tool. This parameter works as a threshold for the regularization process, preventing diffusion over edge features stronger than the threshold value. Hence, you use this parameter to prevent the noise reduction process from damaging significant image detail and features. Smaller values are more aggressive to protect edges against blurring caused by the diffusion process. Larger values are more permissive and lead to smoother images, but may cause some detail loss.

* Smoothness

Controls the continuity degree of smooth regions. Smaller values tend to preserve small-scale structures such as sharp edges, but also may generate staircase artifacts. The default value of 2 usually works well with most images, and you normally won't need to change it.

* Iterations

TGVDenoise emulates a diffusion process, following a dynamic evolution scheme. Each iteration can be seen as a time sample, where the image reaches a new state. The process converges to an optimal solution when no significant diffusion can happen between neighbor pixels. This usually requires a large number of iterations. Convergence is evaluated by calculating the norm of the difference between two consecutive iteration steps. This is an estimate of global error, or mean square change. Usually, once this difference drops below 1/255, little or no changes can be perceived in the displayed image.

Upon execution, TGVDenoise writes its final global error estimate to the console as a 'delta' value. You can watch these estimates to get an idea of how well the process is converging to an acceptable result. The default value of the iterations parameter (100 iterations) is a good starting point for most images.

* Automatic convergence

When automatic convergence is enabled, the process will be stopped if either the difference between two successive iterations (see the iterations parameter above) is smaller than the convergence parameter (see below), or if the maximum number of iterations is reached; whichever happens first. If this parameter is disabled, the specified number of iterations will always be performed, ignoring the convergence parameter.

Be aware that when automatic convergence is enabled, the TGVDenoise process is not previewable. In other words, since convergence is specific to the data being processed, there is no guarantee that the result obtained on a preview be the same as the result obtained on its mother image with the same parameters. Depending on the image, the differences can be significant. For this reason the automatic convergence parameter is disabled by default.

* Convergence

When automatic convergence is enabled, the TGVDenoise process will be stopped when the difference between two successive iterations becomes smaller than this value. See the information given for the iterations and automatic convergence parameters.

Local Support Parameters

A local support image changes the rate of the diffusion process, creating a spatially dependent function. This allows to better preserve high SNR areas, or to protect specific image edges. For deep-sky astronomical images, a local support is advisable to achieve optimal results, especially for linear images.

It is very important to point out that the local support image is not a mask. The support image should represent the signal-to-noise ratio of the target image, where higher pixel values will provide stronger protection. This effectively controls the dynamic properties and evolution of the TGV regularization process, but does not work by mixing processed and unprocessed pixels, so it has nothing to do with a mask. Of course, the TGVDenoise process is fully maskable, so you can apply it through a mask if you find it necessary, in addition to using (or not) a local support image.

* Preview

Enable this option to get a preview of the working local support image that will be used by TGVDenoise. This is a control mode that must be disabled to perform an actual noise reduction process.

* Support image

By default, the local support image is disabled. TGVDenoise performs outstandingly well for most images without the help of a local support. When enabled, the local support is the intensity component (in the HSI color system) of the target image by default, but you can select any grayscale image with the same geometry as the target view (or its mother image in the case of a preview). When SNR is proportional to illumination, as happens with deep-sky images, a local support can be of great help to drive the TGVDenoise process, as noted above.

* Noise Reduction

This is the number of small-scale wavelet layers that will be removed from the working local support image. For very noisy images, smoothing the local support may help to achieve more accurate results because this makes it less dependent on spurious small-scale variations.

* Midtones

This is the midtones balance parameter of a histogram transformation that will be applied to the working local support image. Use it to fine tune protection on moderately bright areas.

This is the shadows clipping point parameter of a histogram transformation that will be applied to the working local support image. Increase it to decrease protection on dark areas.

* Highlights

This is the highlights clipping point parameter of a histogram transformation that will be applied to the working local support image. Decrease it to increase protection on bright areas.

Important Notes

* TGVDenoise is a memory intensive process. Internally it requires 13 working duplicates of one channel of the target image in 32-bit floating point format, so it may easily cause out-of-memory errors on the 32-bit version of PixInsight for Windows. If this happens to you, the only solution is a 64-bit version of PixInsight running on a 64-bit operating system.

* TGVDenoise is processor-intensive. We have done our best to achieve the best optimized parallel implementation we are capable of, but despite this, the algorithm requires significant processing time for moderately large images. Since the process is fully previewable (please note: if the automatic convergence parameter is disabled), you should use relatively small previews defined on special interest areas of the image to find an optimal set of parameters, which you can apply to the whole image after testing them thoroughly.

* The strength, edge protection and smoothness parameters have been implemented as exponential controls on the TGVDenoise interface. We have already used exponential controls in the AdaptiveStretch tool. These controls allow you to work in scientific notation, which gives you quick and easier control for parameters with very large ranges. Each parameter is divided into four fields that work in tandem: a large edit field where you can enter the parameter's value directly, a small edit field to enter the coefficient in the range from 1 to 9.99, a slider to change the coefficient value with the mouse, and a spin box where you can define the exponent. In future versions of the TGVDenoise tool, most of these parameters will probably be implemented differently, once we improve our knowledge of their practical ranges.

References

[1] Florian Knoll, Kristian Bredies, Thomas Pock and Rudolf Stollberger. Second order total generalized variation (TGV) for MRI. Magnetic Resonance in Medicine 65(2):480-491, 2011.

[2] Kristian Bredies, Karl Kunisch and Thomas Pock. Total generalized variation. SIAM Journal on Imaging Sciences, 3(3):492-526, 2010.

[3] Kristian Bredies. Recovering piecewise smooth multichannel images by minimization of convex functionals with total generalized variation penalty. Submitted for publication, 2012.

[4] Kristian Bredies, Yiqiu Dong and Michael Hintermüller. Spatially dependent regularization parameter selection in total generalized variation models for image restoration. Submitted for publication, 2012.

[5] Chambolle, A., Pock, T. A first-order primal-dual algorithm for convex problems with applications to imaging. J. Math. Imaging Vis. 40, 120–145, 2011.

15
##### General / Re: Batch Processing and Rotator Angle
« on: 2013 May 07 08:38:05 »
Quote
If I image a target on both sides of the meridian

If the only reason to rotate your camera is a meridian flip, then you shouldn't do that if you preprocess your data with PixInsight. Our StarAlignment tool corrects for meridian flip automatically.

Pages: [1] 2 3 ... 259