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Why stacking reveals faint details
A faint target may contribute only a small signal to each exposure. In one frame, that signal can be obscured by shot noise and other variation. Once the frames are aligned, the target lands in corresponding places, while much of the random noise does not. Combining the frames therefore makes the shared signal more distinct relative to the noise.
The improvement is about separating captured signal from variation, not simply making an image brighter. Siril’s tutorial illustrates a nebula that looks indistinct in one frame and shows more detail as registered frames are added: Siril’s signal-to-noise tutorial.
What the square-root rule means
In the general model described by Siril, stacking N comparable frames improves SNR approximately in proportion to √N. This is an idealized relationship, not a guarantee for every set of real exposures. Doubling the number of frames gives about √2, or 1.41 times, the SNR under that model—not twice the SNR. Gains continue, but each additional doubling brings a smaller improvement than the previous one.
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What stacking cannot fix
Stacking does not automatically increase optical resolution. Focus, optics, atmospheric seeing, tracking errors, and the quality of the source frames constrain the detail in the result. Excluding poor frames can improve effective sharpness compared with including them, but the outcome depends on the data and does not imply a universal resolution gain. Alignment also involves resampling pixels, so registration is not a way to recreate information absent from the exposures.
What happens before and after integration
Integration is the step that combines the aligned light frames, but it sits within a broader workflow. Calibration, registration, frame review, and post-stack processing address different problems and should not be confused with one another.
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Calibration removes some systematic patterns
Bias, dark, and flat frames can help correct sensor or optical patterns in the light frames. Their usefulness depends on capturing and applying them appropriately; a mismatched dark or a poor flat can introduce artifacts rather than remove them. Calibration is not a substitute for sound capture.
Registration aligns the target
Registration shifts and, where appropriate, transforms frames so the same stars or target features correspond before combination. Siril’s registration guide describes alignment in its preprocessing workflow. Some correction options depend on available astrometric information.
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The linear stack is usually an intermediate result
A calibrated stack can remain linear, with pixel values not yet adjusted for display. It may look nearly black even though faint signal is present. Siril’s official preprocessing guide notes that this is normal at the end of preprocessing. Histogram stretching and other processing make captured signal visible on screen; they do not mean the stack itself created new data.
How to stack astrophotography frames
- Capture multiple light frames of the same target. Keep the original camera data where possible. Siril’s scripted stacking tutorial describes DSLR RAW and astronomy-camera FITS light frames. Its example equipment setup is an illustration, not a minimum requirement.
- Capture suitable calibration frames. Use bias, dark, and flat data as appropriate to the camera and workflow. Check that calibration data and settings are suitable for the lights you are correcting.
- Calibrate the lights and address gradients. Correct relevant sensor and optical patterns. Gradients caused by changing light pollution or moonlight can vary during a long session; Siril’s preprocessing guide notes they may be easier to handle on individual images before stacking.
- Register the preprocessed sequence. Align frames so the target features land in corresponding locations. If using distortion correction, confirm the required astrometric information is available.
- Review frame quality. Consider measures such as FWHM, star roundness, background, and detected-star count. Exclude or down-weight poor frames when their quality warrants it; more exposures do not automatically make a better stack if some are substantially blurred or otherwise defective.
- Choose the combination and rejection settings. Select a method suited to the frame type and decide whether outlier rejection is appropriate. Rejection can suppress transient defects such as satellite trails or hot pixels when the settings and number of frames support it.
- Process the linear result for display. Crop registration borders, remove residual gradients if needed, calibrate color, stretch the histogram, and apply sharpening or deconvolution conservatively. Siril’s workflow tutorial includes post-stack processing and cautions that excessive deconvolution can introduce artifacts and noise.
Choosing a stacking method
There is no single best combination setting for every data set. Frame type, image quality, and whether you prioritize noise reduction or outlier handling all matter.
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| Choice | What it does | When to consider it |
|---|---|---|
| Mean (average) | Combines corresponding pixel values by averaging them. The Siril 1.4.4 stacking documentation generally prefers mean stacking for light frames. | Use as a common approach when the light frames are suitable and outlier handling is managed through appropriate rejection settings. |
| Median | Uses the middle value at each pixel, making it more robust to some outliers. Siril’s 1.4.4 documentation describes its SNR growth as about 0.8√N, compared with the general √N model for averaging. | Consider when robustness to outliers is important, while accounting for the lower SNR gain stated in the documentation. |
| Normalization and rejection options | Adjust how frame values are treated and can reject outlying pixel values; effects depend on the selected method and settings. | Choose settings based on the data and whether transient defects need handling rather than assuming one configuration fits every sequence. |
These options and their behavior are documented in the Siril 1.4.4 stacking documentation. Its median SNR estimate is documentation guidance, not a promise for every data set. Weighting frames by image quality or integration time can also help prevent weaker frames from contributing as much as stronger ones.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Which frames should you keep?
Quantity helps when added frames are comparable and useful, but frame quality matters too. Siril’s preprocessing and stacking tools offer criteria such as FWHM, star roundness, background, and detected-star count, as well as weighting by image quality or integration time. These are ways to assess and manage a sequence, not universal thresholds for accepting or rejecting an exposure.
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- Inspect frames for blur, poor star shapes, cloud, or an unusually poor background.
- Reject or reduce the influence of a frame when its defects make it less useful than the rest.
- Use outlier rejection to target transient pixel-level defects, rather than treating it as a fix for broad problems affecting an entire exposure.
Software and workflow flexibility
Siril is an open-source astronomical image-processing application whose official site listed version 1.4.4, released June 17, 2026, when consulted. It supports calibration, registration, stacking, outlier rejection, color calibration, deconvolution, and histogram stretching. Its release information and supported platforms can change; check the official Siril website for current details.
Siril supports both scripted and manual workflows. Scripts can automate repeated preprocessing and stacking, while manual controls allow stages to be inspected and adjusted. The interface instructions in its stacking tutorial were made with version 0.99.8.1, so labels and steps may differ in newer releases.
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