Face Hallucination

13 papers with code • 1 benchmarks • 2 datasets

Face hallucination is the task of generating high-resolution (HR) facial images from low-resolution (LR) inputs.

( Image credit: Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination )

Most implemented papers

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

xinntao/ESRGAN 1 Sep 2018

To further enhance the visual quality, we thoroughly study three key components of SRGAN - network architecture, adversarial loss and perceptual loss, and improve each of them to derive an Enhanced SRGAN (ESRGAN).

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

adamian98/pulse CVPR 2020

We present an algorithm addressing this problem, PULSE (Photo Upsampling via Latent Space Exploration), which generates high-resolution, realistic images at resolutions previously unseen in the literature.

Context-Patch Face Hallucination Based on Thresholding Locality-constrained Representation and Reproducing Learning

junjun-jiang/Face-Hallucination-Benchmark 3 Sep 2018

To this end, this study incorporates the contextual information of image patch and proposes a powerful and efficient context-patch based face hallucination approach, namely Thresholding Locality-constrained Representation and Reproducing learning (TLcR-RL).

Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation

ZhaoJ9014/face.evoLVe.PyTorch 26 May 2019

Recent deep learning based face recognition methods have achieved great performance, but it still remains challenging to recognize very low-resolution query face like 28x28 pixels when CCTV camera is far from the captured subject.

DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

andreas128/NTIRE21_Learning_SR_Space 9 Apr 2020

To the best of our knowledge, DeepSEE is the first method to leverage semantic maps for explorative super-resolution.

Deep CNN Denoiser and Multi-layer Neighbor Component Embedding for Face Hallucination

ZoieMo/Multi-task 28 Jun 2018

Most of the current face hallucination methods, whether they are shallow learning-based or deep learning-based, all try to learn a relationship model between Low-Resolution (LR) and High-Resolution (HR) spaces with the help of a training set.

SiGAN: Siamese Generative Adversarial Network for Identity-Preserving Face Hallucination

jesse1029/SiGAN 22 Jul 2018

Despite generative adversarial networks (GANs) can hallucinate photo-realistic high-resolution (HR) faces from low-resolution (LR) faces, they cannot guarantee preserving the identities of hallucinated HR faces, making the HR faces poorly recognizable.

HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

Lotayou/Face-Renovation 11 May 2020

Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with heterogeneous degradations and rich background contents.

Face Hallucination via Split-Attention in Split-Attention Network

mdswyz/SISN-Face-Hallucination 22 Oct 2020

However, most of them fail to take into account the overall facial profile and fine texture details simultaneously, resulting in reduced naturalness and fidelity of the reconstructed face, and further impairing the performance of downstream tasks (e. g., face detection, facial recognition).

Deep Learning-based Face Super-Resolution: A Survey

junjun-jiang/Face-Hallucination-Benchmark 11 Jan 2021

Second, we elaborate on the facial characteristics and popular datasets used in FSR.