Summary: In this paper we present parallel algorithms to solve the problem of image restoration when the Point Spread Function is Space Variant. The problem has a very high computational complexity and it is very hard to solve it on scalar computers. The algorithms are based on the decomposition of the image spatial domain and on the solution of both constrained and unconstrained restoration subproblems of size smaller than the original. The main results can be summarized as follows: (a) the quality of restorations do not depend on the number of subdomains; (b) the unconstrained restoration is scalable and efficient even with a large number of processors while the constrained restoration is efficient for subdomains of more than \(50\times 50\) pixels. The numerical tests have been executed on a Cray T3E with 128 processors and on a network of workstations.