Taylor-Guided Iterative Gradient Projection Neural Network for Coal-Dust Scanning Electron Microscopy Super Resolution

This article proposes an interactive-interpretable network (IIN) to facilitate accurately zooming in the low-resolution scanning electron microscopy (SEM) image data which could preserve the intricate details of original image without long exposure of coal-dust specimens under intense energy radiation. By harnessing the interpretability benefits of traditional model-driven approaches, the proposed data-driven deep neural network facilitates an interactive super-resolution (SR) process unfolding as signal processing optimization procedures. According to the iterative proximal strategy, a deep unfolding way with proximal gradient projection is employed, in which each layer plays as a step to integrate deep networks into classic optimization with more obviously augmenting clarity and interpretability. Leveraging Taylor series approximation, the SR intermediates are decomposed into fundamental (low-order), derivative (high-order) components, and Remainder term, which are informative by intrinsic prior knowledge to elucidate varying image frequency details. Also, Taylor Remainder is treated as intermediate residual through the discrepancy measurement between intermediate high-resolution part and the whole-order information aggregation, which serves as a guidance for the following interactive refinement. Additionally, the reconstructed outputs undergo further synergy of the dual-model framework that could enhance final SR outcomes. Final experiments show that the proposed method with the interpretable and accurate merits, which outperforms other highly related SR methods from quantitative and qualitative perspectives.