Abstract
Hyperspectral fluorescence microscopy images of biological specimens frequently contain multiple observations of a sparse set of spectral features spread in space with varying intensity. Here, we introduce a spectral vector denoising algorithm that filters out noise without sacrificing spatial information by leveraging redundant observations of spectral signatures. The algorithm applies an n-dimensional Chebyshev or Fourier transform to cluster pixels based on spectral similarity independent of pixel intensity or location, and a denoising convolution filter is then applied in this spectral space. The denoised image may then undergo spectral decomposition analysis with enhanced accuracy. Tests utilizing both simulated and empirical microscopy data indicate that denoising in 3 to 5-dimensional (3D to 5D) spectral spaces decreases unmixing error by up to 70% without degrading spatial resolution.