An adaptive block based un-sharp masking for image quality enhancement

Mortezaie, Zahra; Hassanpour, Hamid; Amiri, S. Asadi

doi:10.1007/s11042-019-7594-4

Cited by 4 publications

(1 citation statement)

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“…In this paper, we intend to use both global and local features to properly reconstruct high-frequency details and essential features of the face image. Extracting the edges of the face image using the Local Binary Pattern (LBP) [42] and Unsharp Masking (UM) [43] techniques and then adding them to the input image as a preprocessing can help the proposed model to accurately extract the highfrequency information of the LR image. In addition, we use the Self-Attention mechanism residually to retrieve face components correctly.…”

Section: Proposed Methodsmentioning

confidence: 99%

Empowering Face Recognition Methods using a GAN-based Single Image Super-Resolution Network

Shahbakhsh¹,

Hassanpour²

2022

IJE

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Face recognition is one of the most common authentication techniques widely used due to its easy access. In many face recognition applications, captured images are often of low resolution. Face recognition methods perform poorly on low resolution images because they are trained on high resolution face images. Although existing face hallucination methods may generate visually pleasing images, they cannot improve the performance of face recognition methods at low resolution as the structure of the face image and high-frequency details are not sufficiently preserved. Recent advances in deep learning have been used in this paper to propose a new face super-resolution approach to empower face recognition methods. In this paper, a Generative Adversarial Network is used to empower face recognition in low-resolution images. This network considers image edges and reconstructs high-frequency details to preserve the face structure. The proposed technique to generate super-resolved features is usable in any face recognition method. We have used some state-of-the-art face recognition methods to evaluate the proposed method. The results showed a significant impact of the proposed method on the accuracy of face recognition of low resolution images.

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Section: Proposed Methodsmentioning

confidence: 99%