Multichannel sampling of low light level scenes with unknown shifts
Images captured under low-light conditions are noisy as a result of photon statistics and quantization error, among other reasons. Such statistical limitations can be reduced by using pixels with larger areas, but this approach leads to aliasing artifacts. We propose a maximum-likelihood version of super-resolution for low-light conditions in which Fourier image coefficients and unknown spatial shifts between captured frames are estimated iteratively, all in order to produce the single image with high expected fidelity. We illustrate the power of our method on both one-dimensional synthetic data and on two-dimensional medical images.