A SUBPIXEL IMAGE-RESTORATION ALGORITHM

In statistical image reconstruction, data are often recorded on a regu lar grid of squares, known as pixels, and the reconstructed image is d efined on the same pixel grid. Thus, the reconstruction of a continuou s planar image is piecewise constant on pixels, and boundaries in the image consist of horizontal and vertical edges lying between pixels. T his approximation to the true boundary can result in a loss of informa tion that may be quite noticeable for small objects, only a few pixels in size. increasing the resolution of the sensor may not be a practic al alternative. If some prior assumptions are made about the true imag e, however, reconstruction to a greater accuracy than that of the reco rding sensor's pixel grid is possible. We adopt a Bayesian approach, i ncorporating prior information about the true image in a stochastic mo del that attaches higher probability to images with shorter total edge length. in reconstructions, pixels may be of a single color or split between two colors. The model is illustrated using both real and simul ated data.