It is known that the distributions of wavelet coefficients of natural images at different scales and orientations can
be approximated by generalized Gaussian probability density functions. We exploit this prior knowledge within
a novel statistical framework for multi-frame image restoration based on the maximum a-posteriori (MAP) algorithm.
We describe an iterative algorithm for obtaining a high-fidelity object estimate from multiple warped,
blurred, and noisy low-resolution images. We compare our new method with several other techniques including
linear restoration, and restoration using Markov Random Field (MRF) object priors. We will discuss the
performances of the algorithms.
Proc. SPIE. 5817, Visual Information Processing XIV
KEYWORDS: Point spread functions, Detection and tracking algorithms, Imaging systems, Sensors, Image restoration, Image resolution, Digital imaging, Reconstruction algorithms, Data communications, Binary data
We describe a new algorithm for combining multiple low-resolution images to obtain a high-resolution object estimate. Each camera is treated as a communication channel and we exploit sub-pixel shifts to achieve significant resolution enhancement. The 2D4 algorithm is an iterative likelihood-based method that is computationally less expensive than the two-dimensional Viterbi algorithm. In this paper, we modify the 2D4 algorithm and apply it to the multiframe image restoration problem. We demonstrate the reconstruction of a high-resolution scene from multiple blurred, noisy, and shifted low-resolution image measurements. We discuss the modifications and approximations to the 2D4 algorithm that are required to reduce its complexity for this application. We present the performance of this algorithm and compare it with the performance of Iterative Back Projection and optimal linear methods.