Zoom Based Super-Resolution: A Fast Approach Using Particle Swarm Optimization

Given a set of images captured using different integer zoom settings of a camera, we propose a fast approach to obtain super-resolution (SR) for the least zoomed image at a resolution of the most zoomed image. We first obtain SR approximation to the superresolved image using a learning based approach that uses training database consisting of low-resolution (LR) and their high-resolution (HR) images. We model the LR observations as the aliased and noisy versions of their HR parts and estimate the decimation using the learned SR approximation and the available least zoomed observation. A discontinuity preserving Markov random field (MRF) is used as a prior and its parameters are estimated using the SR approximation. Finally Maximum a posteriori (MAP)-MRF formulation is used and the final cost function is optimized using particle swarm optimization (PSO) technique which is computationally efficient compared to simulated annealing. The proposed method can be used in multiresolution fusion for remotely sensed images where the available HR panchromatic image can be used to obtain HR multispectral images. Another interesting application is in immersive navigation for walk through application. Here one can change zoom setting without compromising on the spatial resolution.