Rosette nebula

INTRODUCTION

Astrophotography requires the improvisation of raw images obtained using a camera. A single image is often blurry, dim, or has many defects. This can be corrected by using multiple images that increase the correct light signals. There are many aspects of image processing that this project will turn upon. The main objective of this project is to produce stunning images of rosette nebula from 5 raw .fits images.

Other than the 5 photographic images, we are also given three images - Master dark, Master flat, and Master bias. These are three different images that will be used for calibration. Master bias is an image exposed to short term electronic noise. Master Dark is obtained by a camera with a closed lens cap. This image represents long term exposure to background noise. The master flat is an image that represents optical irregularities in the optics. It is also a long term exposure image but with uniform light exposure.

WORK FLOW

Through various iterations, I found out that master bias doesn't have any substantial effect in the image processing and hence was chosen not to be included in the calibration process. The work will showcase the differences observed in calibrated and uncalibrated images. For doing this, I have considered three different cases -

Case 1: No calibration.

Case 2: Calibration only using master dark

Case 3: Calibration using master dark and master flat

After calibration, image registration is put into account. To do so, I have used the inbuilt MATLAB command imregister. This inbuilt function can intake various arguments. However, since the images are obtained using the same camera the proper choice is using 'monomodal' operation. The registered image is then fed to a convolution filter to smoothen out the disturbances.

Lastly, efforts were made to match the histogram of this developed image with the target image. The developed code is dynamic, in a way, it only requires the images to be fed as input along with the target image. At the end of the code is a set of functions that is being called within the code.

ANALYSIS

It can be seen that calibration using only master dark didnt help but rather calibrating with master dark and master flat both increased the intensity of some of the stars. Now, let us look at how the final images look in all the three cases.

It can be seen clearly that master dark calibrates the image by a large extent. The blurry nature of the image present at the left edge of the Case 1's final image and as seen in the maste dark section (at the begining) has been removed after the calibration. This change is clearly visible in the master dark calibrated image (Case 2). It can also be seen that master flat further greatly calibrates the image. As it is visible that the Case 3's final image is less brighter as compared to Case 2's final image .

The MATLAB code is available here