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Stacking several well-calibrated, aligned deep-sky exposures produces a cleaner master image in which real, repeatable structure is easier to separate from random noise. The reliable sequence is organize, calibrate, register, review, integrate, then stretch and enhance. Stacking cannot recover detail that never reached the sensor, and a poor frame or failed alignment can reduce the quality of the result no matter which integration method you choose.
What stacking changes—and what it cannot do
Every exposure contains the target, sky background, read noise, thermal effects and random variations. Signal from a nebula, galaxy or star field appears in the same place after registration, while much of the random variation changes from frame to frame. Combining registered frames therefore increases the relative strength of consistent signal.
Siril’s stacking documentation states that, for average stacking with rejection, the increase in signal-to-noise ratio (SNR) is proportional to √N, where N is the number of images. This is an idealized statistical relationship, not a promise that every doubled sequence will look twice as clean. Clouds, tracking errors, gradients, focus changes, light pollution and calibration mistakes can dominate the result.
| Number of comparable exposures | Idealized SNR factor (relative to one frame) | Interpretation |
|---|---|---|
| 1 | 1× | Baseline |
| 4 | 2× | About twice the ideal SNR of one frame |
| 9 | 3× | Additional frames still help, but with diminishing returns |
| 16 | 4× | Four times the ideal SNR of one frame |
| 25 | 5× | Useful for weak, extended targets when the frames are consistent |
| 100 | 10× | Requires a large, genuinely usable sequence |
The table describes the mathematical rule only. It does not account for unusable frames, changing sky brightness, residual gradients or clipping. Stacking also does not create stars, dust lanes or color that are absent from the captured data; later stretching makes surviving signal visible but cannot manufacture information.
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Before stacking: build a clean, traceable dataset
Separate lights and calibration frames
Keep the original files untouched and work from copies or a clearly named processing directory. Put target exposures (light frames) in one folder and calibration frames in separate folders for biases, darks and flats when those frames exist. Siril’s tutorial uses this organization because each type corrects a different class of defect.
- Light frames: exposures containing the target and sky.
- Bias frames: very short exposures that characterize the camera’s readout offset, when appropriate for the camera and workflow.
- Dark frames: exposures matched to relevant temperature, gain or exposure conditions to model thermal signal and hot pixels.
- Flat frames: evenly illuminated exposures that record vignetting and dust shadows.
Not every camera session includes every calibration type. Whether a particular frame set is useful depends on the sensor, capture settings and how the data was acquired. A flat-field panel can make flat capture easier, but it is optional capture equipment, not a prerequisite for stacking and not a direct noise-reduction treatment.
Record the sequence conditions
Keep notes on exposure time, gain or ISO, temperature, filter, telescope orientation and the time span of the session. Those details help you identify frames that should not be mixed and explain background changes during quality review. Do not combine files with materially different capture conditions as though they were interchangeable without checking their calibration and background behavior.
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Calibration removes repeatable sensor and optical patterns before alignment and integration. In a typical Siril workflow, master bias, dark and flat files are created or selected and applied to the lights. The purpose is correction, not cosmetic smoothing: a bad master can imprint its own pattern on every calibrated frame.
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- Inspect the calibration files. Confirm that darks match the relevant exposure and camera settings and that flats were captured with the same optical configuration, focus position and filter as the lights.
- Create masters where appropriate. Combining multiple calibration frames reduces random variation in the master itself. Use the calibration workflow provided by your software rather than assuming one recipe fits every sensor.
- Apply calibration to the lights. Keep the calibrated outputs in a separate directory so the originals remain available for recovery.
- Inspect a few calibrated frames. Look for new gradients, clipped areas, dust rings that remain unexpectedly strong or edge artifacts. If calibration makes the image worse, stop and verify the master construction and matching conditions.
Siril’s tutorial notes that a session may lack flats. That does not mean flats are universally unnecessary; it means the available workflow must reflect what was actually captured. Missing calibration data cannot always be recreated after the fact.
Register (align) the calibrated lights
Registration determines how stars or other stable scene features move between exposures and creates aligned files for integration. It also accounts for optical distortion in the Siril workflow. Alignment is essential because averaging unaligned stars turns point sources into trails and can erase faint structure.
- Load the calibrated light sequence into your stacking application.
- Run its registration or star-alignment operation using the current interface and the detection settings appropriate to your field.
- Review the generated aligned images, not just the completion message.
- Check the corners and center at high magnification for doubled stars, elongated stars, sudden shifts or cropping that removes useful data.
Faint stars, changing framing and severe distortion make registration harder. If the sequence contains a large pointing jump, verify that the software found enough common stars and that its chosen reference frame is sharp and representative. Do not assume a successful process dialog proves that every frame aligned correctly.
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Review quality and decide whether to weight frames
A stack is only as strong as the frames that enter it. Siril’s manual tutorial presents a quality graph with measurements such as FWHM, roundness, weighted FWHM and background value. Use those measurements to find frames affected by clouds, wind shake, poor focus, tracking faults, satellite trails or an unusually bright background.
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Reject clearly defective frames
Exclude an exposure when inspection shows a defect that will remain after integration: severe star elongation, an obvious focus failure, a passing cloud, a large obstruction or a calibration artifact. A slightly noisier but well-tracked frame can still contribute useful signal; do not discard data solely because one metric is less attractive.
Weight usable frames when quality differs
Weighting gives selected frames more influence instead of treating every exposure equally. Siril lists star count, weighted FWHM, measured noise and integration time as possible weighting bases. Each captures a different aspect of quality:
- Star count can indicate detection quality, but a bright background or a crowded field can distort the comparison.
- Weighted FWHM reflects sharpness while accounting for the distribution of star profiles.
- Measured noise favors cleaner frames, though the measurement can be affected by gradients and sky brightness.
- Integration time reflects exposure contribution, but a longer exposure is not automatically better if it is trailed or clouded.
No single metric is universally best. Compare the graph with the actual images and keep the weighting basis consistent with the defect you are trying to control.
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For deep-sky work, the usual starting point is an average (mean) integration with outlier rejection. The software computes a mean from the registered pixels while excluding values that look inconsistent with the rest of the sequence. Normalization can first bring frames to a comparable level when background or exposure levels differ.
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| Method or option | What it does | Deep-sky implication |
|---|---|---|
| Average with rejection | Calculates a mean after rejecting detected deviant pixel values | Strong general starting point when the sequence is calibrated and registered |
| Median | Uses the middle value at each pixel rather than a mean | A different estimator; behavior changes with stack size, outliers and signal retention, so there is no universal threshold or winner |
| Normalization | Adjusts frame levels toward a reference before rejection or combination | Useful when changing backgrounds would otherwise make rejection less meaningful |
| Weighting | Lets selected frame qualities contribute more to the result | Helpful for mixed-quality sequences when the chosen metric reflects real defects |
| Simple sum | Adds pixel values without normalization or rejection in Siril’s documented method | Siril documents this for planetary processing; it is not the default recommendation for deep-sky integration |
Normalization and rejection settings depend on the sequence and the implementation. A changing moonlit background, thin cloud or strong gradient can require a different choice from a stable, dark session. Compare the integrated result with and without normalization when the background level changes noticeably, and inspect whether faint structure is being retained.
Siril-centered procedure
Siril’s official documentation and tutorial provide a concrete example of the workflow above. Interface labels can change; the software’s current 1.4.4 release is dated June 17, 2026, so verify the labels in the version you have installed.
- Create the project folders. Keep lights, bias, dark and flat files separate and preserve the originals.
- Generate or select masters. Use the calibration tools to build the masters your camera and session support, then calibrate the lights.
- Register the calibrated sequence. Run star-based registration and inspect the aligned files for doubled or distorted stars.
- Open the quality view. Examine FWHM, roundness, weighted FWHM, background and related measurements. Mark unusable frames for exclusion.
- Set optional weighting. Choose a basis such as star count, weighted FWHM, noise or integration time only when it matches the quality decision you need to make.
- Integrate with an average and rejection method. Decide whether normalization is needed because frame levels differ. Avoid the documented sum method for ordinary deep-sky integration.
- Inspect the linear master. Look for residual trails, gradients, clipped channels, dark rings and unusual rejection patterns before stretching.
The click-by-click names and parameter locations are specific to Siril. Other applications may use different terminology, defaults and rejection algorithms; transfer the processing logic, not the menu names or example thresholds.
Check rejection and diagnose an abnormal stack
After integration, inspect the rejection information if your software provides it. Siril’s manual tutorial uses a rejection rate between 0.1% and 0.5% as a check in its example workflow. Those values belong to that tutorial’s sequence and settings, not to every camera, target or stack.
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If rejection is much higher than expected
- Check for clouds, satellite trails, changing gradients or a mixture of incompatible exposure conditions.
- Verify that registration succeeded; misalignment can make normal star pixels look like outliers.
- Confirm that the calibration masters match the lights.
- Review low and high sigma factors. In the Siril tutorial, increasing a sigma factor lowers the rejection rate.
If rejection is nearly zero
- Look for settings that are too permissive to remove genuine outliers.
- Confirm that the sequence contains enough comparable frames for rejection to be meaningful.
- Inspect the linear result for satellite trails, hot pixels and other defects that remain visible.
Do not optimize for a particular percentage without examining the image. The goal is a clean, faithful integration, not a number that looks reassuring in a report.
Reveal faint detail after integration
The integrated master is normally still linear: most of its useful signal is compressed into a narrow range near the dark end of the histogram. A nearly black-looking file can contain substantial information. Stretching redistributes that information so the eye and display can see it.
- Inspect the linear master first. Check stars, gradients and channel clipping before applying strong curves.
- Correct gradients and background issues. Remove or reduce uneven illumination before an aggressive stretch, because stretching also amplifies unwanted structure.
- Apply color calibration. Calibrate color after integration using the tools appropriate to your camera, filters and target.
- Stretch gradually. Use histogram, curves or another controlled transfer function. Make several small adjustments rather than one extreme move that clips highlights or crushes the background.
- Enhance selectively. Siril lists operations including deconvolution and histogram stretching among post-stack enhancement stages. Apply sharpening or deconvolution conservatively and inspect star halos and noise between steps.
- Revisit the integration if necessary. If stretching exposes trails, residual hot pixels or a lopsided background, return to frame selection, calibration or rejection settings instead of trying to hide the defect with stronger processing.
Color calibration and stretching reveal signal that survived capture and integration; they do not replace additional exposure time. If the target remains indistinct after a reasonable stretch, the limiting factor may be insufficient total integration time, sky brightness, focus, tracking or calibration quality.
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Common failure modes and fixes
| Symptom | Likely cause | First corrective action |
|---|---|---|
| Stars are doubled or smeared | Registration failed or the frames were not registered | Re-run alignment, verify the reference frame and inspect the aligned outputs |
| Large blotches or rings | Missing or mismatched flats, dust changes or poor calibration | Check flat capture and master matching; keep affected frames separate if the optical setup changed |
| Stack is darker or brighter at one side | Sky gradient, changing illumination or inconsistent normalization | Inspect backgrounds, normalize when appropriate and correct gradients after integration |
| Trails remain in the master | Satellite, aircraft or hot-pixel outliers were not rejected | Review rejection settings and remove the offending frame when necessary |
| Stars look soft despite many frames | Low-quality frames dominated the average or weighting favored the wrong metric | Exclude poor frames and compare weighted and unweighted integrations |
| Noise increases dramatically after stretching | Very little total signal, excessive stretch or residual background structure | Use a gentler stretch, correct gradients and add more consistent exposure time when possible |
How to decide whether to add more exposures
Because ideal SNR follows √N, improvements become progressively smaller: increasing from one to four frames doubles the ideal SNR, while increasing from 100 to 121 frames improves the ideal factor only from 10× to 11×. Additional frames are still valuable when they are clean and consistent, but a dozen sharp, well-calibrated exposures can outperform a larger set dominated by clouds, tracking errors or changing backgrounds.
Quick Recap
- Add frames when the target is limited by random noise and the new exposures match the existing calibration and optical setup.
- Prioritize consistency when deciding between a marginal frame and no frame.
- Separate sessions with different filters, sensors, framing or radically different sky conditions and integrate them deliberately rather than mixing them blindly.
- Keep the raw files and processing notes so you can change frame selection without repeating data capture.
A repeatable checklist
- Back up the original lights and calibration files.
- Separate lights, biases, darks and flats according to what the session actually contains.
- Build matching masters and calibrate the lights where appropriate.
- Register the calibrated lights and inspect alignment across the field.
- Review FWHM, roundness, background and visual defects.
- Exclude clearly damaged frames; use weighting only when its metric reflects real quality.
- Integrate with an average and rejection approach for ordinary deep-sky work, adding normalization when frame levels differ.
- Check rejection behavior and the linear master.
- Correct gradients, perform color calibration and stretch gradually.
- Save the integrated master and the settings used so the result can be reproduced or revised.
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