Denoising broadband CARS hyperspectral images on variance-stabilized data: a unified benchmark

Authors:
Preprint
Publisher website
Google Scholar
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.
Funding Information
  • Department of Energy BER (DE-SC0022121)

This publication has 46 references indexed in Scilit: