Dense spatial sampling is required in high-resolution optical imaging and many other biomedical optical imaging
methods, such as diffuse optical imaging. Arrayed photodetectors, in particular charge coupled device cameras are
commonly used mainly because of their high pixel count. Nonetheless, discrete-element photodetectors, such as
photomultiplier tubes, are often desirable in many performance-demanding imaging applications. However, utilization of
the discrete-element photodetectors typically requires raster scan to achieve arbitrary retrospective sampling with high
density. Care must be taken in using the relatively large sensitive areas of discrete-element photodetectors to densely
sample the image plane. In addition, off-line data analysis and image reconstruction often require full-field sampling.
Pixel-by-pixel scanning is not only slow but also unnecessary in diffusion-limited imaging. We propose a superresolution
method that can recover the finer features of an image sampled with a coarse-scale sensor. This generalpurpose
method was established on the spatial transfer function of the photodetector-lens system, and achieved superresolution
by inversion of this linear transfer function. Regularized optimization algorithms were used to achieve
optimized deconvolution. Compared to the uncorrected blurred image, the proposed super-resolution method
significantly improved image quality in terms of resolution and quantitation. Using this reconstruction method, the
acquisition speed with a scanning photodetector can be dramatically improved without significantly sacrificing sampling
density or flexibility.