A robust facial image super-resolution model via mirror-patch based neighbor representation

Rajput, Shyam Singh; Arya, K. V.

doi:10.1007/s11042-019-07791-y

Cited by 16 publications

(1 citation statement)

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“…In the first category, the system is trained to reconstruct a patch of an image at a time. Rajput and Arya propose mirror-patch based neighbor representation (MPNR) system for face hallucination [15]. Their findings suggest that this system is capable of filling missing parts in the face image, as well as super resolving noisy inputs.…”

Section: Introductionmentioning

confidence: 99%

Capsule GAN for robust face super resolution

Majdabadi

2020

Multimed Tools Appl

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Face hallucination is an emerging sub-field of Super-Resolution (SR) which aims to reconstruct the High-Resolution (HR) facial image given its Low-Resolution (LR) counterpart. The task becomes more challenging when the LR image is extremely small due to the image distortion in the super-resolved results. A variety of deep learning-based approaches has been introduced to address this issue by using attribute domain information. However, a more complex dataset or even further networks is required for training these models. In order to avoid these complexities and yet preserve the precision in reconstructed output, a robust Multi-Scale Gradient capsule GAN for face SR is proposed in this paper. A novel similarity metric called Feature SIMilarity (FSIM) is introduced as well. The proposed network surpassed state-of-the-art face SR systems in all metrics and demonstrates more robust performance while facing image transformations.

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Section: Introductionmentioning

confidence: 99%