Denoising broadband CARS hyperspectral images on variance-stabilized data: a unified benchmark
Year: 2026
Type: preprint
Abstract: Significance Broadband coherent anti-Stokes Raman scattering (BCARS) enables label-free hyperspectral chemical imaging, but many signal components are small compared to the accompanying heteroscedastic noise, making optimal denoising crucial. Aim We seek to identify the highest-performing hyperspectral image denoising methods for BCARS. Approach We derive a physics-based, variance stabilization transform that yields nearly intensity-independent BCARS noise variance while preserving the dispersive Raman response. Within this framework, we compare six denoisers using a shared phase-retrieval pipeline: singular value decomposition, BM4D, Noise2Noise, deep image prior, self-supervised diffusion, and a cross-covariance-based singular value decomposition (CCV) introduced here. Evaluation uses five simulated cubes with exact ground truth and experimental measurements of glycerol, polystyrene beads, and Caenorhabditis elegans . Results In simulations, the variance stabilization transform reduces the ratio of maximum to minimum channel noise variance from approximately 72 to 1.2. Deep image prior and self-supervised diffusion denoisers achieve the highest mean foreground peak signal-to-noise ratios. CCV matches the mean foreground structural similarity of deep image prior with substantially shorter runtime and no per-cube rank tuning. Experimental comparisons reveal sample-dependent performance. Conclusions Physics-based variance stabilization provides a common framework for adapting and evaluating hyper-spectral denoisers for BCARS. CCV offers a practical balance of reconstruction fidelity, computational speed, and automated operation.
Authors: Haoyu Xu, Marcus T. Cicerone
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7222681529