Super-resolution reconstruction based on multisource bidirectional similarity and non-local similarity matching

A novel super-resolution (SR) reconstruction algorithm based on multisource bidirectional similarity and non-local similarity matching for multi-exposure dynamic image sequences is proposed in this study. First, luminance compensation for multi-exposure dynamic images is implemented based on multisource bidirectional similarity. Then, combining a self-adaptive regional correlation evaluation strategy and a weighting strategy based on pseudo-Zernike moment feature similarity and structural similarity yields a novel and robust non-local similarity matching scheme (PZ-NSM) to learn non-local similarity priors between low- and high-resolution dynamic image patches at different spatio-temporal scales without additional training. Finally, SR reconstruction is implemented based on PZ-NSM and spatio-temporal trans-scale fusion of non-local similarities between dynamic images. The proposed algorithm does not rely on accurate estimation of subpixel motion and can therefore be adapted to more complex motion patterns. It has high rotation invariance effectiveness and is robust to noise and illumination. Experimental results demonstrate that the proposed algorithm outperforms existing algorithms in terms of both subjective and objective evaluations.