Self-calibrated method removes mixed noise and stripes in hyperspectral images
Self-Calibrated Subspace Robust PCA for Parameter-Free Removal of Mixed Noise and Line Stripes in Hyperspectral Images
Machine Learning
Summary
Hyperspectral images, which capture detailed color data beyond normal pictures, often have different types of noise like random spots and line-like stripes. The authors developed a method that automatically estimates the noise characteristics without needing the user to set parameters. Their approach can clean these images efficiently and better than previous methods, even when the noise types and levels vary across the image. It works faster and adapts to different noise patterns in each color band, making it easier to get clear hyperspectral images.
What this means in practice
- •For remote sensing engineers: Improve cleaning of hyperspectral satellite or aerial imagery by removing complex mixed noise without needing manual tuning per image.
- •For industrial inspection teams: Automatically fix noise and stripe artifacts in hyperspectral data collected for material or product quality control, speeding up analysis.
Authors
Wenzhe Wang
Abstract
Hyperspectral images (HSIs) are often corrupted by Gaussian noise, impulses and stripes whose intensities vary across bands. The representative coefficient correlated total variation (RCCTV) model restores HSIs efficiently, but it lacks Gaussian and stripe terms, and its noise-aware extensions require the noise level and a stripe budget to be specified in advance. This article proposes a self-calibrated RCCTV (SC-RCCTV) model, in which the noise parameters are estimated adaptively during the iterations. A band-wise dense term and line-constant column and row stripe terms are introduced into RCCTV. The noise level of each band is initialized by a stripe-invariant median absolute deviation estimator and refined online from the residual, which turns the separation of sparse and dense noise into a band-adaptive Huber loss, and the stripe thresholds follow from a significance test whose false-alarm rate can be predicted analytically. No parameter is tuned for a specific image, and every subproblem of the alternating direction method of multipliers (ADMM) solver is solved in closed form. Under line stripes, SC-RCCTV outperforms the parameter-free FastHyMix by 8.17 dB on native-size cubes, and, without any noise information, it exceeds S3TTV, which is given the true noise parameters, by 1.52 dB while running more than 1,900 times faster. On average, it stays within 0.01 dB of the same model given the true noise levels, and its online calibration improves on a two-stage estimate-then-denoise scheme. Experiments on real Indian Pines and Urban data further delineate its scope for stripes that do not span entire lines.