Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Vector Space Projections: A Numerical Approach to Signal and Image Processing, Neural Nets, and Optics
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Convex restriction sets for CBERS-2 satellite image restoration
International Journal of Remote Sensing
A genetic programming framework for content-based image retrieval
Pattern Recognition
A Method of Self-Adaptive Inertia Weight for PSO
CSSE '08 Proceedings of the 2008 International Conference on Computer Science and Software Engineering - Volume 01
Content-based image retrieval by combining genetic algorithm and support vector machine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Image restoration by convex projections using adaptive constraintsand the L1 norm
IEEE Transactions on Signal Processing
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Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (@l) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of @l can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PSO)-POCS image restoration algorithm, in which the @l value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms.