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A single dataset, reimagined through multiple palettes. From cool OIII blends to fiery SHO tones, this project explores how color transforms the Pacman Nebula’s structure, mood, and story.

About the Target: The Pacman Nebula (NGC 281)

The Pacman Nebula (NGC 281) is a bright, sculpted star‑forming region in the constellation Cassiopeia, named for its unmistakable resemblance to the classic arcade character. Located roughly 9,500 light‑years away, it sits along the Perseus Arm of the Milky Way and is packed with glowing hydrogen clouds, dense dust lanes, and young, energetic stars carving out cavities in the surrounding gas.

Narrowband imaging reveals the nebula’s complex internal structure — from the sharp ridges of ionized sulfur to the soft, billowing hydrogen clouds and the delicate oxygen filaments woven throughout. This target is a favorite among astrophotographers because it responds beautifully to different color palettes, each highlighting a different aspect of its anatomy.

Image Acquisition

This project was captured over multiple nights using two complementary narrowband filter sets: the Antlia ALP‑T and the Askar Color Magic D2 series. Together they provided deep, clean signal across Hα, OIII, and SII while still taking advantage of the convenience of an OSC camera. The combination of both filter systems allowed for flexible palette creation, stronger channel separation, and improved overall signal‑to‑noise — especially in the fainter OIII structures of the Pacman Nebula. Long integration times across both filters ensured that the bright emission regions and the delicate outer wisps were captured with enough depth to support multiple color interpretations.

Across both filter stacks, I collected:

• 1140 minutes of Antlia narrowband data
• 1085 minutes of Askar narrowband data
• Additional RGB broadband frames for natural star color

The long integration time was intentional — the Pacman Nebula contains both bright emission regions and faint, wispy structures that only emerge with deep exposure. The combination of two filter systems also helped balance signal‑to‑noise across channels, especially in OIII, which tends to be weaker in this target.

All data was calibrated, registered, and stacked in Siril, producing clean masters for each channel before moving into the processing phase.

Processing Workflow

Processing was done entirely in Siril, with a focus on channel normalization and palette flexibility. Because this project showcases multiple color interpretations (SHO, HOO, and hybrid blends), the workflow was built around creating matched, normalized masters that could be recombined consistently across palettes.

1. Channel Extraction

Using PixelMath, I matched the Hα and OIII masters to the SII master as the reference. This ensured that each channel shared a comparable brightness scale, preventing color dominance and making palette switching far more predictable.

2. Palette Construction

Each final image in the post represents a different palette:

• SHO (Hubble Palette) — SII → R, Hα → G, OIII → B Highlights structural detail and produces the classic gold‑and‑blue aesthetic.
• HOO (Bi‑color Oxygen‑Forward) — Hα → R, OIII → G/B Produces a natural, cooler look with teal oxygen regions and warm hydrogen cores.
• Hybrid Palette — A blend of SHO mapping with HOO‑style oxygen emphasis Balances dramatic structure with softer, more natural color transitions.

Each palette received its own PixelMath block, applied to the normalized masters to generate a clean RGB composite.

3. Post‑Combination Adjustments

After assembling each palette through PixelMath, the composites went through a series of structural and noise‑control steps. Because this dataset was captured under light‑polluted skies, noise management was a major focus. Instead of traditional denoising methods, I used Syqon Prism noise‑reduction model, which excels at suppressing chromatic noise and gradients without smearing fine nebular detail. This model made a noticeable difference in the faint OIII regions and helped clean up the background while preserving the sharpness of the emission ridges.

Other adjustments included:

• Background extraction to remove residual gradients
• Local contrast enhancement to bring out the carved cavities and dust lanes
• Star reintegration using RGB stars for natural color

No SPCC or color calibration was used in this workflow — all color shaping was done manually to preserve the artistic intent of each palette.

4. Final Color Refinement in Photoshop

Once the structural work was complete, each palette received its final color polish in Photoshop using Selective Color and Hue/Saturation. These tools allowed for precise, controlled adjustments that enhanced the personality of each palette:

• Selective Color refined the golds, teals, and blues by targeting specific color groups without affecting the rest of the image.
• Hue/Saturation provided gentle global and targeted adjustments to balance intensity, tame greens, and enhance faint oxygen tones.

These finishing touches gave each palette its own identity — dramatic gold‑blue contrast in SHO, cool teal emphasis in HOO, and a smooth cinematic blend in the hybrid version.

False‑Color Imaging in Astrophotography

All of the palettes in this project are forms of false‑color imaging, a technique widely used in professional astronomy — including by the Hubble Space Telescope. Narrowband filters isolate specific emission lines (Hα, OIII, and SII), and those signals are then mapped to visible colors to highlight structure, contrast, and detail that would otherwise be invisible. Each palette in the gallery represents a different way of assigning those channels to RGB, revealing unique aspects of the nebula while preserving the underlying scientific data.

Palette Gallery

HOO Palette (Blue‑Cyan Core, Red Ridges)

OIII‑Matched Dominant Palette

Standard SHO (Flame‑Tone Palette)

Hybrid HOO

Cool SHO (OIII‑Matched Palette)

SHO Variant (Red Fury Palette)

A journey through the Pacman Nebula’s many moods (short video)

Final Thoughts

This project was my first real dive into creating an SHO image—and one of my earliest experiences working with the Askar Color Magic filter—so it became a crash course in how much control narrowband data really offers. I learned how important it is to normalize each channel, how combining OIII from both filters can strengthen the signal, and just how powerful PixelMath can be for shaping a palette to match the dominant emission line, whether Ha, OIII, or SII. Along the way I also built my first HOO image and began to understand how each palette tells a different story. Exploring all these variations from a single dataset made this one of the most rewarding processing sessions I’ve had so far.

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All images captured and processed by Alexis Antonio — alexisantonio.com