Literature Review on Single Image Super Resolution

Sahu, Pankaj; Bazal, Surya; Dubey, Shalini

doi:10.31142/ijtsrd18339

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2019

2023

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“…Image Super-resolution (ISR) is premised on the concept that a High-Resolution (HR) image or image sequence can be created by combining Low-Resolution (LR) noisy images [1]- [5]. Super-Resolution (SR) is an emerging domain with remarkable evolution to address the practical utility and feasibilityrelated challenges.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Deep Convolution Networks Based Image Super-Resolution Techniques

Sindge¹,

Dutta²,

Saini³

2023

Artificial Intelligence and Communication Technologies

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Single Image Super-Resolution (SISR) is gaining huge attention in the digital age across various application domains such as surveillance, medical imaging, and agriculture. Numerous SR methods based on deep learning were used by existing researchers to improve image resolution. Literature shows that deep convolutional neural networks (CNNs) perform exceptionally well to handle degraded images. In this study, CNN-based methods from a deep learning environment are compared to reconstruct the High-Resolution (HR) images. Observations show that SRCNN and FSRCNN can achieve considerable image quality after reconstruction; however, performance is limited to small datasets due to shallow network parameters. Furthermore, VDSR and LapSRN were also utilized against heavy datasets due to their huge computational efficiency.

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Section: Introductionmentioning

confidence: 99%