Super‐resolution image reconstruction using molecular docking

Abstract

Molecular-docking is an essential tool in the drug designing process, where a small molecule ligand (drug) binds with disease-causing protein molecule to prevent its further activity. Docking process helps in predicting the most appropriate configuration and the optimal interaction energy between the interacting molecules (ligand and protein) to form a stable complex. Based on this idea, a new learning-based single image super-resolution reconstruction (LSI-SRR) method is proposed here. Estimation of a high resolution (HR) patch is achieved by optimising the interaction energy between the input low resolution patch and its corresponding candidate patches appropriately chosen from the training image dataset via Genetic algorithm. Structured-spatiogram based measure; a new and competent similarity criterion is proposed to select potentially efficient training images which encompass better statistical and structural co-relation with the input image. The proposed method is tested on synthetic and real-time images at different magnification factors. Performance analysis of the proposed work is compared with some of the representative state-of-the-art LSI-SRR methods. Experimental results demonstrate that the proposed method produces HR images with enhanced image details, minimal artefacts and most importantly enables an efficient trade-off between the image qualities to speed than the competing methods.