Fluorescence lifetime imaging microscopy (FLIM) is a microscopic imaging technique to present an image of fluorophore lifetimes. It circumvents the problems of typical imaging methods such as intensity attenuation from depth since a lifetime is independent of the excitation intensity or fluorophore concentration. The lifetime is estimated from the time sequence of photon counts observed with signal-dependent noise, which has a Poisson distribution. Conventional methods usually estimate single or biexponential decay parameters. However, a lifetime component has a distribution or width, because the lifetime depends on macromolecular conformation or inhomogeneity. We present a novel algorithm based on a sparse representation which can estimate the distribution of lifetime. We verify the enhanced performance through simulations and experiments.
Color transforms are important methods in the analysis and processing of images. Image color transform and its inverse transform should be reversible for lossless image processing applications. However, color conversions are not reversible due to finite precision of the conversion coefficients. To overcome this limitation, reversible color transforms have been developed. Color integer transform requires multiplications of coefficients, which are implemented with shift and add operations in most cases. We propose to use canonical signed digit (CSD) representation of reversible color transform coefficients and exploitation of their common subexpressions to reduce the complexity of the hardware implementation significantly. We demonstrate roughly 50% reduction in computation with the proposed method.
We present a color correction algorithm for histogram equalized images captured by a digital camera. Current color
correction methods are based on human color perception of luminance and hue. However, these techniques do not
consider nonlinear camera characteristics, therefore the resulting color images show color distortions where brightness
modification is severe. We propose a new effective color correction method that depends on the camera brightness and
color curve. It utilizes the relationship of luminance and color variation of a camera used for image capture. We can
predict chrominance variation after luminance change by tracing the brightness-chrominance curve of the camera model.
Therefore the resulting image shows color that would have been obtained at different exposure using the same camera.
We verify that the processed images have natural color and that they are similar to images taken at different exposure
conditions. Moreover it is possible to apply the proposed method to software bracketing; we can change the exposure
condition of an image at post processing stage. All test results demonstrate that our method is accurate and useful in the
enhancement of a color of digital images.