Saliency-directed color image interpolation using artificial neural network and particle swarm optimization
In this study, a saliency-directed color image interpolation approach using artificial neural network (ANN) and particle swarm optimization (PSO) is proposed. First, a high-quality saliency map of a color image to be interpolated is generated by a modified block-based visual attention model in an effective manner. Then, based on the saliency map, bilinear interpolation and ANNPSO interpolation are employed for non-saliency (non-ROI) and saliency (ROI) blocks, respectively, to obtain the final color interpolation results. In the proposed ANNPSO interpolation scheme, ANN is used to determine the orientation of each 55 image pattern (block), whereas PSO is employed to determine the weights in 55 interpolation filtering masks. The proposed approach is applicable to image interpolation with arbitrary magnification factors (MFs). Based on the experimental results obtained in this study, the color interpolation results by the proposed approach are better than those by five comparison approaches.
color image interpolation artificial neural network (ann) particle swarm optimization (pso) visual attention model bilinear interpolation interpolation filtering mask magnification factor image super-resolution