destripe 0.1.3

PDHG-based universal stripe-noise removal for images, backed by PyTorch

destripe

PDHG-based stripe-noise removal for NumPy images, backed by PyTorch. The solver decomposes an image into a TV-regularized clean component and directional ℓ²-penalized stripe components.

Features

• Removes vertical and diagonal stripe patterns with five directional components.
• Accepts grayscale (H, W), single-channel (H, W, 1), and RGB (H, W, 3) arrays.
• Preserves input shape and dtype; integer outputs are clipped to their dtype range.
• For RGB inputs, estimates stripes on Rec. 601 luminance and subtracts them from each channel.
• Supports n x n tiled processing with cosine-blended overlap for large images.
• Uses CUDA when available if device=None; otherwise falls back to CPU.

Install

pip install destripe

Quick Start

from destripe import destripe

image = ...  # numpy.ndarray, shape [H, W] or [H, W, 3]

clean = destripe(
    image,
    mu1=0.33,
    mu2=0.003,
    iterations=500,
    tiles=1,       # >1 for n x n tiled processing
    device="cpu",  # "cpu", "cuda", or None to auto-select
)

Parameters

• image: numeric NumPy-compatible array with shape (H, W), (H, W, 1), or (H, W, 3).
• mu1 (default 0.33): TV weight. Higher smooths more and removes stronger stripes; loses fine detail.
• mu2 (default 0.003): ℓ² stripe penalty. Higher extracts stripes more eagerly; can leak real structure.
• iterations (default 500): maximum PDHG iterations.
• tol (default 1e-5): relative-change tolerance for early stopping, checked every 20 iterations.
• tiles (default 1): number of tiles per side. Use values greater than 1 when the image does not fit in memory or stripes are locally non-stationary.
• overlap (default 64): requested blend width in pixels. The solver clamps it to at most one quarter of each tile dimension.
• device (default None): "cpu", "cuda", a torch.device, or None to auto-select CUDA when available.
• proj (default True): project the normalized clean component onto [0, 1].
• verbose (default False): print iteration progress.

Suggested mu Pairs

• Conservative / subtle stripes: [0.1, 0.001], [0.1, 0.0017]
• Light, thin stripes: [0.17, 0.003], [0.23, 0.003]
• Typical to strong stripes: [0.33, 0.003], [0.4, 0.007]
• Severe corruption / short stripes: [0.5, 0.017]

Starting points, not universal optima.

Reference

• https://github.com/NiklasRottmayer/General-Stripe-Removal