A novel approach to multi-frame image super resolution using an innovative filter and pixel selection mechanism

Dongxiao Zhang, Ni Tang, Juhao Gao

Abstract: Deep learning-based methods excel at single image super resolution (SR), but struggle with multi-frame image SR due to their inability to effectively exploit the complementary information within low resolution (LR) images. Graph cuts remain effective in this context. A major challenge for graph cuts lies in the need for the energy function (EF) to adhere to specific regularization constraints. To meet these constraints, existing methods often rely on approximations that can degrade reconstruction quality. To minimize these negative effects, we design a filter to convert the EF into a standard form suitable for graph cuts and introduce a filter-based SR model with maximum accuracy. To fully utilize the complementary information within LR images, we also propose a LR pixel selection mechanism that selects and weights LR pixels in our model. Experimental results demonstrate the robustness of our model against noise and point spread function misestimation. Moreover, our model outperforms existing algorithms in reconstructing fine-grained details.