  We present a two-channel deconvolution method that decomposes images into a
parametric point-source channel and a pixelized extended-source channel. Based
on the central idea of the deconvolution algorithm proposed by Magain, Courbin
& Sohy (1998), the method aims at improving the resolution of the data rather
than at completely removing the point spread function (PSF). Improvements over
the original method include a better regularization of the pixel channel of the
image, based on wavelet filtering and multiscale analysis, and a better
controlled separation of the point source vs. the extended source. In addition,
the method is able to simultaneously deconvolve many individual frames of the
same object taken with different instruments under different PSF conditions.
For this purpose, we introduce a general geometric transformation between
individual images. This transformation allows the combination of the images
without having to interpolate them. We illustrate the capability of our
algorithm using real and simulated images with complex diffraction-limited PSF.
