This paper proposes a new image restoration technique, in which the resultingregularized image approximates the optimal solution steadily. The affect of the regular-ization operator and parameter on the lower band and upper band energy of the residueof the regularized image is theoretically analyzed by employing wavelet transform. Thispaper shows that regularization operator should generally be lowstop and highpass. So thispaper chooses a lowstop and highpass operator as regularization operator, and constructan optimization model which minimizes the mean squares residue of reg ularized solutionto determine regularization parameter. Although the model is random, on the conditionof this paper, it can be solved and yields regularization parameter and regularized solu-tion. Otherwise, the technique has a mechanism to predict noise energy. So, without noiseinformation, it can also work and yield good restoration results.
The authors consider generalized statistically self-affine recursive fractals K with random numbers of subsets on each level. They obtain the Hausdorff dimensions of K without considering whether the subsets on each level are non-overlapping or not. They also give some examples to show that many important sets are the special cases of their models.
A fast algorithm is proposed to solve a kind of high complexity multi-objective problems in this paper. It takes advantages of both the orthogonal design method to search evenly, and the statistical optimal method to speed up the computation. It is very suitable for solving high complexity problems, and quickly yields solutions which converge to the Pareto-optimal set with high precision and uniform distribution. Some complicated multi-objective problems are solved by the algorithm and the results show that the algorithm is not only fast but also superior to other MOGAS and MOEAs, such as the currently efficient algorithm SPEA, in terms of the precision, quantity and distribution of solutions.
Zeng San-you, Ding Li-xin, Kang Li-shanDepartment of Computer Science,China University of GeoSciences, Wuhan 430074, Hubei, China
