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A Review of Single Image Super Resolution Techniques using Convolutional Neural Networks

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Abstract

Single Image Super- Resolution (SISR) is a complex restoration method to recover high-resolution (HR) image from degraded low-resolution (LR) form. SISR is used in many applications, such as microscopic image analysis, medical imaging, security and surveillance, astronomical observation, hyperspectral imaging, and text image super-resolution. Convolutional Neural Networks (CNNs) are most widely used technique to solve Super-Resolution (SR) problems. This paper presents review of SISR methods based on CNN. The SISR CNN models are analyzed based on the design and their performance on benchmark datasets: Set 5, Set 14, BSD 100, and Urban 100. Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) are used for quantitative analysis. ESRGAN model shows the best results on all benchmark datasets and reconstructs images with good visual quality at large upscaling factors. The model performs excellently with PSNR 27.03 dB and SSIM 0.8153 on the Urban 100 dataset for ×4 upscaling factor. The models are further analyzed on the basis of the loss function, scalability, processing time, and number of parameters. The framework and implementation setup of SISR CNN models are also discussed. Perceptual loss function can help to boost the network performance by increasing the visual quality of the reconstructed images. Hence, it has emerged as a new research trend in recent years. It is also observed that there is tremendous growth in the field of blind or unsupervised SISR. The research has shifted to developing reference less performance evaluation parameters for unsupervised SISR.

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Authors would like to express their gratitude to the anonymous reviewers for their insightful and productive feedback.

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  1. Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, MP, India

    Monika Dixit & Ram Narayan Yadav

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Dixit, M., Yadav, R.N. A Review of Single Image Super Resolution Techniques using Convolutional Neural Networks. Multimed Tools Appl 83, 29741–29775 (2024). https://doi.org/10.1007/s11042-023-16786-9

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