DeepSNR - Noise Reduction Tool

Overview

DeepSNR is a deep-learning-based denoising tool for high-quality astrophotography images. It is designed to remove noise while preserving fine details, making it ideal for processing raw integration results.

⚠ Best used immediately after image integration. Avoid using it on already processed images, as it assumes uncorrelated noise.

Parameters

Strength

Controls how much noise is removed by blending the original and denoised images using a linear combination.

Model Version

• v1 (RGB Only): Works only on RGB images.
• v2 (RGB and Grayscale): Supports both RGB and monochrome images and is generally recommended for most cases.

Both models are trained primarily on monochrome (CCD) sensor data.

🚨 For color sensor data (Bayer matrix), use CFA Drizzle integration before applying DeepSNR.

Linear Data (Checkbox)

If checked, DeepSNR automatically applies a stretch to the image before denoising and de-stretches it afterward.

If no STF (Screen Transfer Function) is applied, it will auto-stretch the image before processing. This usually works well, but results may vary.

Usage Tips

• ✅ Apply only to high-quality raw data right after integration.
• ✅ Works best on uncorrelated noise. If your data has structured noise (e.g., walking noise from dithering issues), artifacts may appear.
• ✅ Tolerates hot and cold pixels fairly well.
• ✅ v2 handles column artifacts better, but it’s still recommended to fix them before denoising.
• 🚫 Do not use on heavily processed images—this tool is not meant for aggressive noise reduction after post-processing.

Known Problems

• ⚠ When applied to linear images, high-intensity regions (such as stars) are not well preserved. Better results are usually achieved on stretched images. Be aware of this effect when processing data.