Face Hallucination

5 papers with code · Computer Vision

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 )

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Greatest papers with code

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

CVPR 2020 adamian98/pulse

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

FACE HALLUCINATION IMAGE SUPER-RESOLUTION

DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution

9 Apr 2020mcbuehler/DeepSEE

We validate DeepSEE for up to 32x magnification and exploration of the space of super-resolution.

FACE HALLUCINATION SUPER-RESOLUTION

HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment

11 May 2020Lotayou/Face-Renovation

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 IMAGE DENOISING IMAGE RESTORATION IMAGE SUPER-RESOLUTION

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

3 Sep 2018junjun-jiang/TLcR-RL

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).

FACE HALLUCINATION

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

28 Jun 2018ZoieMo/Multi-task

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.

FACE HALLUCINATION SUPER-RESOLUTION