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Coin-sized Micro-needles Patch to deliver Insulin: An innovation by Shanghai University Scientists

Stem Cells Help Researchers Study The Effects of Pollution on Human Health

A Novel and Robust Muscle Activity Onset Detection Technique by Using an Unsupervised Electromyogram Learning Framework

Early Modern Humans and Neanderthals were close bedfellows

FANCD2 and REV1 Cooperate in the Protection of Nascent DNA Strands in Response to Replication Stress

Spiders' Foraging Strategies Have Cascading Effects on Litter Decomposition Rates

Yingli Announces Sale of 18.8 MW Solar Power Plant in the UK to NextEnergy Solar Fund

China's Award Winning Desertification Control in Kubiqi Desert

Earthquake Early Warning System for Nepal

Mechanical Coupling Mechanism of a Mechanical Force-sensing Channel Protein Discovered by IBP Scientists

Jiahui International Hospital and Brigham and Women's Hospital to Co-Develop Women's Health Center of Excellence in Shanghai

Big Grain1: The ‘Mr. BIG’ for Crops to Grow Bigger by Regulating Auxin Transport in Rice

When there is no Queen in the house, Asian Hive Bees Avoid Risky Foraging for Reproduction

UNICEF HK joins hands with Government to help Mothers sustain Breastfeeding

Fresenius Medical Care Springs into Action after Deadly Tianjin Explosion

In this paper, an effort is made to propose an effective image super resolution (SR) approach to recover a high resolution (HR) image from a single low resolution (LR) image. This approach is based on an iterative back projection (IBP) method with the edge preserving infinite symmetric exponential filter (ISEF) and difference image. Amalgamation of ISEF and difference image provides high frequency information. This approach is applied on different type of images and compared results with different existing image SR approaches. Simulation results demonstrate that proposed approach can more precisely enlarge the LR image. This proposed approach decreases mean square error (MSE) and mean absolute error (MAE) and increases the peak signal-to-noise ratio (PSNR) significantly compared to other existing approaches.