Abstract
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 super-resolution 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 super-resolution 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.