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Topics - Carlos Milovic

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Gallery / 2019 Solar eclipse from Punta Colorada, Chile
« on: 2019 July 09 15:54:43 »
Dear all,

This is the final version of the 2019 eclipse with my data. HDR composition using 7 bracketed frames (1/8000s to 4s) with a Sigma 150-600mm at 400mm and a Canon 80D camera. Tracking with a CG5 mount. Hope you enjoy it!

General / MOVED: ERRORS... ERRORs...ERRORs!!!
« on: 2016 April 12 11:12:58 »

Off-topic / Deep optical images of Malin 1 reveal new features
« on: 2015 December 10 05:40:54 »

Done with PixInsight. :)
Also, Malin 1 is the greatest known spiral galaxy, now even bigger!

New Scripts and Modules / New development module: TGV
« on: 2015 September 08 08:36:28 »
This deserved a new topic, so here it is.

DISCLAIMER: This is a development module. Before installing it, make a buckup of your current (official release) TGV module (in PixInsight/bin). Compatibility of process icons and projects are not guaranteed. Also, many things could change in future releases, including the GUI and internal behaviour.

The download links:


Installation options:
a) Replace the current TGV module in PixInsight/bin with the files above. NOT RECOMMENDED
b) In PixInsight, go to Manage Modules, and unistall TGV. Close PI and launch it again. Copy the new TGV module to a new folder (for example, create: PixInsight/development ). Install the module from that folder.

DISCLAIMER: As said before, we do not guarantee compatibility with previous icons or projects that contains instances of TGVDenoise. The internal behaviour of this new process is a bit different, and some parameters have being renamed or changed. Also, more parameters have being added.

The current TGV module has 2 processes (we'll add a third in the next days, TGVRestoration, for deconvolutions). Below you'll find a brief explanation of them:


- TGVDenoise
This process represents the next "generation" for TGVDN. More powerfull and flexible. The user interfase changed a lot from previous releases. Now there are three groups of parameters. Those related to the Regularization process (the noise reduction itself), the Image Model (how does the noise distribute) and the parameters that controls the iterations. Let's review these parameters.

+ Image Model: We'll begin describing the parameters of this section for a simple reason: You should set these parameters at the start, and don't worry too much about finetunning them. In most cases, you'll just need to adjust later the regularization parameters.

Noise model: Here you have three options: Gaussian, L1 Norm and Poisson. This option governs the inner engine, how the algorithm estimates the degree it can trust the pixel values. The gaussian option assumes that there is a single, additive source of noise. L1 Norm assumes that there are only a few pixels that are too far away from the real value. Poisson, on the other hand, assumes that we are dealing with linear images, with dominating photon noise (and thus, noise has greater amplitude for brighter samples). In practice, Gaussian should be a better choise for non-linear images, while Poisson should be better for linear ones. L1 Norm, does a fairly good job on both cases.

Additive noise: This value should be equal to the standard deviation of the additive noise. You may measure it from an homogeneus zone of the image, ideally the background sky. If you are using the Poisson model, this could be the readout noise (in the normalized range).

SNR strength: Probably this is the most counter-intuive parameter in this process. This parameter introduces a non-linear term in the detection of edges. Basically, it tries to model the photon noise, by varying the amplitude of the standard deviation depending on the brightness of the object. For a single frame, with unit gain, this value should be 1.0 (the ideal Poisson process). For a stack of many images, you should lower the value. For single images that has being rescaled, it should be higher than 1.0. To disable it, set to zero (or the lower possible value). Please consider that this non-linear term is used for every noise model avalaible. The model option you had before tells the algorithm how to trust the data. Both the Additive noise and SNR strength parameters tell the algorithm the amplitude of the expected noise at a given pixel. In practice, you'll see that the SNR strength parameter allows you to target more specifically wheter you want to smooth more the shadows or the highlights.

+ Regularization parameters:

Weight: This is equivalent to the old Strength parameter. Weight is a better name, because it weights the regularization process (the noise reduction) against the data fidelity. Once you have set the Image model parameters, this should be the most critical parameter to fine-tune.

Edge protection: Although the name is the name as in the previous release, now this behaves in a different way. This is a measure in standand deviations where to let the diffusion process to happen, or, in other words, where to set the edge detection. In practice, you may fine tune this paramer in the 1 to 3 range. Our advice is to use a fixed value, and just work with the Weight parameter.

Shapness: This is similar to the old Smoothness parameter. We changed the name because it was somewhat misleading. In reality, this terms controls the balance between the first and second order changes in the diffusion process. What this means, is that it controls how flat or oscilating the small gradients should be.  This, in turn, affects also the sharpness of some borders. So, if you want to increase the sharpness of strong borders, and promote flat surfaces, you should increase this value. To allow more oscillations, which in turn may generate broader borders (maybe fuzzier), decrease it. In practice this parameter should be between 0.5 and 2.0.

+ Iterations parameters

They are self-explanatory, and beheave in the same way as in the previous release. As always, use from 100 to 300 iterations for quickly try parameters, but for a true convergence of the algorithm more than 1000 are recommended. More iterations do not mean that the noise reduction is going to be stronger. You may think as all the TGV processes as a simulation of the diffussion process of fluids. The number of iterations is the time (seconds) you are allowing the experiment to go on.
To have a better understanding of this, we have included a new option, "Preview window" that will shoud the current state of the solution at every iteration step.


- TGVInpaint
This process replaces the contents of pixels with the information that comes from nearby pixels. It is similar to what the DefectMap and CosmeticCorrection performs, but with some advantages: The diffusion process is more aware of the gradients of the image, and thus is better at preserving some structures (although texture is not replicated). For this process to work is vital to provide a "mask" that indicates which pixels to replace. In this process, this mask should be black for those pixels, and white for the ones you want to keep untouched.
The "Precondition" parameter is a filter that is applied before the diffusion process, to help it calculate the gradients of the image. Without it, the process may still work, but should need a more carefull fine-tunning of the parameters, and in some cases, introduce wild oscillations that prevents the algorithm to reach a good convergence.
The Edge diffusion parameter is a threshold that sets the amplitude of the smallest true edges of the images. This is the same as the old Edge protection in the previous release of TGVDenoise.
The Noise reduction parameter works like the Weight parameter in TGVDenoise, but only for those pixels that are marked with white in the mask. By default, noise reduction is disabled (set to 0.0).
The number if iterations in this process could be low, to preserve more the oscilations (and a bit of texture) in your inpainted areas. Increase it to create smoother regions.

In the case of noisy images, you may consider to complement this process with NoiseGenerator, to emulate the texture of inpainted regions. You may use the same images provided here as mask, but inverted for NoiseGenerator.


- TGVRestoration
This process is the application of the TGV regularizator to deconvolution problems. Most of the parameters work in the same way as in TGVDenoise (see the section Filter Parameters), with a few clarifications:

Weight: When we were working with the TGVDenoise process, this parameter was related to the strength of the noise reduction. If too high, the noise reduction would be too high. If too low, no changes were noticeable. In TGVRestoration it is still related to the strength of the noise reduction, but too low values do not return the original image. Instead, it returns a pure deconvolution, non-regularized.

Noise model: Here we have 4 options.
a) Gaussian: This model should work better with planetary images, and other high SNR images.
b) Poisson: This implements the data fidelity term using the Chambolle/Pock primal-dual algorithm. It uses an additive, non-linear term to modify the image at each iteration step.
c) EM - two stages: This is a variation of the Expectation-Maximization algorithm for the Total Variation regularizator, using TGV. The Expectation step is similar to an iteration of the Richardson-Lucy deconvolution, while the Maximization step is a iteration of the TGV noise reduction. Both steps are additive.
d) Regularized RL: This is a more direct variation of the Richardson-Lucy deconvolution, regularized by a TV function. In this case the regularizator acts as a multiplicative factor.
All three later options should work fine for astronomical deep sky images. How TGV has being incorporated is different, as is the deringing algorithm used at each case.

Deringing: This algorithm is different to the one found in the Deconvolution process. Here it acts as a limitation to the changes that are allowed in the deconvolution step (not in the noise reduction step). The deringing local support is usually tied to the Global deringing parameter, with the exception of the Regularized RL algorithm where they act in a different way. The algorithms implemented as deringing are experimental, so we'll like to hear from your experiences.


I hope you like them. Since they are development processes, we are open to suggestions, critics, and bug reports. :)

Off-topic / Podcast de astronomía en español
« on: 2015 February 03 17:14:57 »
Sorry english friends, this is a post for our spanish companions :)

Ricardo García es un cineasta y divulgador científico chileno que está empezando un nuevo proyecto de divulgación: un podcast sobre astronomía. Su intención es hacer un capítulo semanal, con invitados provenientes del mundo amateur y profesional, en las distintas disciplinas que abarca esta ciencia (y hobby). Y en este, el tercer capítulo me tocó a mi ser el invitado. Espero que les guste, y ayuden a difundir este proyecto.

También lo pueden encontrar en itunes

Announcements / Call for images: Planetary, Lunar and Solar images
« on: 2014 November 17 12:00:35 »
Dear all,

We are starting a new processing tool, for automatic alignment of planetary, lunar and solar images, using the same engine that StarAlignment has (handle distortions, etc). This would be compatible with drizzle, of course.

Right now we need small sets of samples (no more than 50 frames) of some typical lunar, solar and planetary targets. If there are large distortions due to atmospheric turbulence, it wold be great.

We plan to release a first version of this tool the first days of December, so we need your help with these small samples to create the core feature extraction algorithm, and test the feature descriptors used in the sample matching.

Thank you very much for sharing your data with us!

Image Processing Challenges / Deconvolution Challenge
« on: 2014 April 16 09:07:07 »
Today we start a new challenge, to test our new deconvolution tool against the old ones. So, we ask you to give your best shot to the test images that we'll post here. The best results are going to be included here. Please try to use all the algorithms avalaible right now (Constrained Least Squares or Wiener filters in RestorationFilter and Regularized versions of Richardson-Lucy and Van Cittern in Deconvolution, specially).

Test 1: Bigradient image - without noise
This is a simple test, without noise to complicate things. Do your best to get sharp edges and as less ringing artifacts as you can.

Test 2: Bigradient image - with gaussian noise
To make things more difficult, let's add a bit of noise to the image. In this case, 10% of gaussian noise. Please, do not use noise reduction algorithm here.

Good luck, and thanks for giving it a try!!!

Sorry english speaking people. This is an spanish event. :)

El equipo organizador del Congreso Austral de Astrofotografía invita al Workshop de procesamiento de Imágenes con PixInsight a realizarse el 23 de noviembre en Santiago de Chile. Este workshop reemplazará las actividades previamente programadas del congreso, debido a problemas de espacio que surgieron a último momento. A continuación el comunicado oficial:

>>  Estimados aficionados :
>>  Lamentamos comunicarles que por motivos ajenos a nuestro control, hemos
>>  tenido que cancelar el Congreso Austral de Astrofotografía 2013 y
>>  modificar drásticamente el programa.
>>  El nuevo programa sólo contempla la realización del workshop de
>> procesamiento de imágenes con PixInsight  para el día sábado 23 de
>> Noviembre. Éste estará a cergo de los Sres. Vicent Peris (Universidad de
>> Valencia) y Carlos Milovic (PUC y Grupo de Desarrollo PixInsight).
>>  El taller constará de mini cursos teóricos de procesamiento de imágenes y
>>  sesiones prácticas, por lo que se recomienda asistir con laptop y el
>>  programa instalado (se puede bajar una versión de prueba de 45 días).
>>  El costo es de $30.000 pesos ( 60 USD) y tendrá una duración de 6 horas
>> lectivas (incluye coffee breaks, almuerzo y once).
>>  El taller tendrá lugar en el Observatorio Roan-Jase, Camino el volcán
>> 29238, San José de Maipo
>>  Santiago
>>  Chile
>> Como referencia, se encuentra pasado el kilómetro 42, a mano izquierda
>> del camino.
>> El inicio será a las 10:00 AM.
>> Hay todavía algunos cupos disponibles, por lo que invitamos a los
>>  interesados a inscribirse pronto!!
>> Esperamos su comprensión por este impasse de última hora.
>> Un cordial saludo,
>> *Equipo Organizador CAA 2013*

New Scripts and Modules / MOVED: StarMonitor
« on: 2013 March 30 12:23:56 »
This topic has been moved to General.

Please use this board to publicy new scripts and modules, not for support or discussion related to them (do not create new topic for such matters).

Gallery / My summer (south. h) season (updated!)
« on: 2013 January 24 10:18:39 »
Hi all

I was finally able to upload the new pictures to the server. I decided to put them all together in this threat instead of making individual ones.

All pictures were taken from Hacienda Los Andes, Chile, between September 2012 and January 2013. Using a modified Canon T2i camera, over an Alt 5 mount. Wide fields (first three images) were made with a Canon 135mm L series lens, at f/4.5 (courtesy of Daniel Verschatse). Other pics were made with a William Optics Megrez 110, at f/4.8.
I'll add exposure time details in a few days. Larger versions of the pictures are linked below.

So, here they are:

Small Magellanic Cloud

Andromeda Galaxy

Large Magellanic Cloud (mosaic)

M45 Pleiades

Witch Head Nebula

Tarantula Nebula

Horsehead Nebula (reprocessed)

Great Orion Nebula

M78 and Bardard's Loop Nebulas

Orion's Belt, Mintaka and Alnilam

****** New Images ***********

Eta Carina Nebula
Rosette Nebula

Cone Nebula

Running Chicken Nebula

Gallery / LMC mosaic
« on: 2012 December 26 12:24:00 »
This is a two panel mosaic of the LMC, done with a Canon 135mm L series lens (property of Daniel Verschatse) at f/4.6.
25x 450s + 23x 450s + 8x 90s
ISO800, Canon T2i with Astrodon filter.
First panel captured in September, the second one in December.
Done in Astrofarm, Hacienda Los Andes, Chile.

Gallery / M45
« on: 2012 December 26 12:18:06 »
The Pleiades.

17x 750s + 16x 90s + 16x 10s + 16x 1s
ISO 800, Canon T2i with Astrodon filter.
W.O. Megrez 110 at f/4.8.
From Astrofarm, Hacienda Los Andes, Chile

This is my first light with this scope. And is the first pic at primal focus I've done in several years. Hope you like it.

Gallery / Andromeda wide field
« on: 2012 October 22 14:26:08 »
Hi guys!

This is the second pic from Hacienda Los Andes (Río Hurtado, Chile). 8*450s images, ISO 800, modded Canon T2i. Canon 135mm f/2.0L lens at f/4.6 (courtesy of Daniel Verschatse).

I hope you like it. Critics are welcome.

Gallery / Small Magellanic Cloud
« on: 2012 October 22 14:25:08 »
Hi guys!

This is one of the pics I took in the last trip to the north of Chile, to Hacienda Los Andes (Río Hurtado). 25*450s images, ISO 800, modded Canon T2i. Canon 135mm f/2.0L lens at f/4.6 (courtesy of Daniel Verschatse).

I hope you like it. Critics are welcome.

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