Multispectral imaging through scattering media and around corners via spectral component separation

Existing methods for imaging through scattering media prioritize grayscale and often falter in resolving multispectral speckles, leading to inadequate spectral recovery. We propose a method that establishes a spectral component separation model for high-quality multispectral imaging through scattering media and around corners. By leveraging the uncorrelation among speckles of different wavelengths and the superposition essence of multispectral speckles, a multispectral speckle simplex with speckles of different wavelengths as vertices is constructed. To resolve these vertices, spectral intensity modulation and a joint-solving mechanism are designed for mutual cooperation. This mechanism employs the Harsanyi-Farrand-Chang method for wavelength number estimation, enhances vertex component analysis with a standby rule for initial solutions, and implements an improved non-negative matrix factorization algorithm for accurate separation. Our method successfully recovers multispectral objects from separated speckles, as confirmed by experiments across six wavelength channels. It is also validated for imaging hidden objects around corners, enhancing surround view functionality. This technique holds significant promise for multispectral imaging in various scattering environments.