Lessons from the Masters: Current Concepts in Astronomical Image Processing (The Patrick Moore Practical Astronomy Series)
Language: English
Pages: 387
ISBN: 1461478332
Format: PDF / Kindle (mobi) / ePub
There are currently thousands of amateur astronomers around the world engaged in astrophotography at a sophisticated level. Their ranks far outnumber professional astronomers doing the same and their contributions both technically and artistically are the dominant drivers of progress in the field today. This book is a unique collaboration of individuals world-renowned in their particular area and covers in detail each of the major sub-disciplines of astrophotography. This approach offers the reader the greatest opportunity to learn the most current information and the latest techniques directly from the foremost innovators in the field today.
“Lessons from the Masters” includes a brilliant body of recognized leaders in astronomical imaging, assembled by Robert Gendler, who delivers the most current, sophisticated and useful information on digital enhancement techniques in astrophotography available today. Each chapter focuses on a particular technique, but the book as a whole covers all types of astronomical image processing, including processing of events such as eclipses, using DSLRs, and deep-sky, planetary, widefield, and high resolution astronomical image processing. Recognized contributors include deep-sky experts such as Jay GaBany, Tony Hallas, and Ken Crawford, high-resolution planetary expert Damian Peach, and the founder of TWAN (The World at Night) Babak A. Tafreshi.
A large number of illustrations (150, 75 in color) present the challenges and accomplishments involved in the processing of astronomical images by enthusiasts.
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Than rods to become active. This is why objects appear colorless in dimly lit situations; without enough light, our cones cannot function. Interestingly, light is comprised of three primary colors, red, blue and green. Of these, the cones in our eyes are most sensitive to the latter. This means the first color we are able to detect is usually green. Astronomical telescopes are essentially used for two purposes: (1) to help separate distant, closely spaced objects and (2) to collect a lot of light.
The Curves tool. The default Channel is RGB, but each of the primary color channels can be selected from the Channel drop-down menu. Step 6: Select a color from the Channel drop-down menu. This will enable the direct modification of the histogram for the selected hue. Step 7: Click on the Curves tool, hold and drag the cursor while watching changes in the Histogram window. Over time, you will be able to correlate changes made with the color tool curve and changes in the histogram’s shape. The goal.
Applied to the entire image. What we care about are the smaller scale structures, and my goal with this galaxy is to define the complete disk within the halo (Fig. 19). Now we need to apply a “hide all” layer mask by having the “High Pass Small” layer selected then Alt-Click the “Add Layer Mask” icon at the bottom of the layer panel. This will create a blank black mask. Because black conceals, none of the pixels of this layer will channel to the base layer because the mask is completely black.
Demonstrate a workflow. I used the 0.8 m Schulman Telescope and an STX 16803 CCD camera atop Mount Lemmon to acquire this data. Figure 2 shows the job script that will perform the steps in the list above for luminance data (the grayscale/unfiltered component of the image). Unlike the human readable version above, some of these steps are broader tools that have multiple functions and require the determination of additional parameters. Ignore the column called “Settings.” This refers to the number of.
Chapter as well as the Photoshop processing constitute 85 % of the rendering of a complete image. Other techniques are usually applied to different types of objects or various kinds of artifacts. However no matter how practiced these steps are, it is the quality of the data that ultimately determines how effective the workflow above can be. Given data with good Signal-to-Noise and few artifacts, I hope you find the steps demonstrated in this chapter are a robust and consistent way to produce.