This paper introduces a novel technique to extract a sparse feature vector from extreme low resolution face images. This feature vector enables us to ...

SSR2: Sparse Signal Recovery for Single-Image Super-Resolution on Faces With Extreme Low Resolutions · Additional Details. Grant Number(s). 2013-IJ-CX-K005.

The proposed super-resolution method is robust to noise and face alignment, and can handle extreme low-resolution faces up to 16x magnification factor with just ...

This paper introduces a novel technique to extract a sparse feature vector from extreme low resolution face images. This feature vector enables us to ...

A Multi-Resolution Convolutional Neural Network (MRCNN) is proposed to address the problem of identify face on low resolution and is increasing the accuracy ...

SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions ; Journal: Pattern Recognition, 2019, p. 308-324 ; Publisher: ...

SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions, R. Abiantun et al. PR 2019. [PDF]. Robust face ...

Mar 1, 2022 · SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low resolutions Pattern Recognit. 2019 90 308-324.

SSR2: Sparse Signal Recovery for Single-Image Super-Resolution on Faces With Extreme Low Resolutions. Date Published. June 2019. Publication Type. Research ...

A Patch Ordering Approach to Single Image Super-resolution Problem · SSR2: Sparse signal recovery for single-image super-resolution on faces with extreme low ...