Development News

New Deconvolution Processes

Deconvolution is our implementation of state-of-the-art regularized Richardson-Lucy and Van Cittert deconvolution algorithms. Simply put, these algorithms separate significant image structures from the noise at each deconvolution iteration. This is possible thanks to specific, wavelet-based multiscale analysis techniques.

At each deconvolution iteration, significant structures are kept and the noise is discarded or attenuated. This allows for simultaneous deconvolution and noise reduction, which leads to robust deconvolution procedures that yield greatly improved results when compared to traditional or less sophisticated implementations. In addition, this tool implements a high-performance deringing algorithm that fixes the well-known Gibbs phenomenon, or ringing artifacts (e.g., dark halos around stars).

Regularized Van Cittert and Wiener deconvolution (the latter implemented by PTeam member Carlos Milovic) are ideal deconvolution algorithms for lunar and planetary images. Regularized Richardson-Lucy is the best option to deconvolve linear deep-sky images.

We have authored two fully-worked, step-by-step processing examples that will help you to integrate this powerful tool in your image processing workflow:

Deconvolution of a High-Resolution Lunar Image, with original CCD raw data acquired by Vicent Peris.

 NGC 5189 from Geminy Observatory South: Deconvolution and HDRWaveletTransform in PixInsight, with original raw data from the Gemini Science Archive.

New HDRWaveletTransform Process

HDRWaveletTransform is a new algorithm for multiscale processing of high dynamic range images. It is a completely new, revolutionary vision of the high dynamic range problem. Created by PTeam's wavelet expert Vicent Peris, HDRWaveletTransform has changed the way we process most deep sky images.

The best part is that this tool is both extremely powerful and very easy to use. You can check this on the following documents:

Introductory step-by-step processing example: Multiscale Processing with HDRWaveletTransform
A Selection of Processing Examples with HDRWaveletTransform

Here is another good example with a high dynamic range luminance image of the M42 region:

Linear combination of exposures from 1 minute to 30 minutes with a modified Canon 350D DSLR @ 400 ISO (6.5 hours total exposure time) and instrument apertures ranging from f/8 to f/2.8. All individual exposures were preprocessed and registered with DeepSkyStacker, and integrated linearly as a 32-bit floating point HDR image with PixelMath in PixInsight. This is the luminance of the original RGB color image.

After HDRWaveletTransform (5 layers, 3x3 Gaussian scaling function, automatic balance).

Images courtesy of Vicent Peris and José Luis Lamadrid. Note that only HDRWaveletTransform has been applied with nearly default settings; no other process has been used to produce the second image linked above. Note also that the HDRWaveletTransform process cannot be applied to the above JPEG image; at least a 32-bit image is necessary to represent the whole original data range.

New GREYCstoration Process

GREYCstoration is a new noise reduction process in PixInsight. It is our implementation of an open-source image regularization algorithm created by David Tschumperlé, a CNRS researcher in the Image Team of the GREYC Lab in Caen, France. This algorithm is based on state-of-the-art image processing methods using nonlinear multi-valued diffusion partial differential equations.

As implemented in PixInsight, GREYCstoration is a high-performance, all-terrain noise reduction tool, able to preserve extremely thin image details, and adaptable to numerous noise types and image restoration requirements. Our implementation is focused on the denoising capabilities of the algorithm; future versions will also cover inpainting and upsampling with GREYCstoration.

GREYCstoration website
GREYCstoration demonstration examples

Highlights

PixInsight and the ALHAMBRA Survey

A team of researchers from institutes all over the world, led by Mariano Moles (Instituto de Astrofísica de Andalucía, CSIC), is currently undertaking a large scale survey at Calar Alto Observatory that will reveal the 90% of the history of the Universe. PixInsight is being used to represent ALHAMBRA fields as they would display if observed by the human eye.

Read press release at Calar Alto website.
See a high-resolution version of ALHAMBRA field F08_P01_1.
ALHAMBRA Survey server

PixInsight at the ECCAI 2006

PTeam Member Carlos Milovic presented the PixInsight project at the East Coast Conference on Astronomical Imaging (ECCAI), held on August 11-13 2006 at Philadelphia, USA.

Download the PixInsight ECCAI 2006 Presentation in PDF format (24 MB).

PixInsight Featured as Cool Application by Trolltech ASA

The creators of the Qt framework have selected PixInsight as a Cool Qt-based Application.

See feature on Trolltech's corporate website.

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