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Super-resolution (SR) imaging is to produce one or a set of high-resolution images from their lower-resolution counterparts. In this paper, our two recent research works are briefly introduced, that is, the Markov chain Monte Carlo (MCMC) SR approach and the state-space SR approach. The MCMC SR approach exploits the MCMC technique for performing the stochastic SR image reconstruction using image samples...
In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully exploited for performing super-resolution image reconstruction. Despite its powerful performance, it usually suffers from that the reconstructed high-resolution image is too smooth to lose much detail information. To further enhance the edge and detail information in the reconstructed high-resolution image, an...
The super-resolution (SR) imaging is to overcome the inherent limitations of the image acquisition systems to produce high-resolution images from their low-resolution counterparts. In our recent work, the Markov chain Monte Carlo (MCMC) technique has been successfully developed and shown as a promising stochastic approach for addressing the SR problem. However, the MCMC SR approach requires substantial...
Recently, the Markov chain Monte Carlo (MCMC) technique has been proved as an effective approach to address the super-resolution image reconstruction problem. However, this approach usually requires a substantial amount of time to generate a sufficiently large number of samples for estimating the unknown high-resolution image. Limiting the simulation time could possibly lead to some artifacts presented...
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