• Publications
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Anchored Neighborhood Regression for Fast Example-Based Super-Resolution
  • R. Timofte, V. Smet, L. Gool
  • Computer Science, Mathematics
  • IEEE International Conference on Computer Vision
  • 1 December 2013
TLDR
This paper proposes fast super-resolution methods while making no compromise on quality, and supports the use of sparse learned dictionaries in combination with neighbor embedding methods, and proposes the anchored neighborhood regression. Expand
A+: Adjusted Anchored Neighborhood Regression for Fast Super-Resolution
TLDR
This work proposes A+, an improved variant of Anchored Neighborhood Regression, which combines the best qualities of ANR and SF and builds on the features and anchored regressors from ANR but instead of learning the regressors on the dictionary it uses the full training material, similar to SF. Expand
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
TLDR
It is concluded that the NTIRE 2017 challenge pushes the state-of-the-art in single-image super-resolution, reaching the best results to date on the popular Set5, Set14, B100, Urban100 datasets and on the authors' newly proposed DIV2K. Expand
Deep Expectation of Real and Apparent Age from a Single Image Without Facial Landmarks
TLDR
A deep learning solution to age estimation from a single face image without the use of facial landmarks is proposed and the IMDB-WIKI dataset is introduced, the largest public dataset of face images with age and gender labels. Expand
Learning Discriminative Model Prediction for Tracking
TLDR
An end-to-end tracking architecture, capable of fully exploiting both target and background appearance information for target model prediction, derived from a discriminative learning loss by designing a dedicated optimization process that is capable of predicting a powerful model in only a few iterations. Expand
DEX: Deep EXpectation of Apparent Age from a Single Image
TLDR
The proposed method, Deep EXpectation (DEX) of apparent age, first detects the face in the test image and then extracts the CNN predictions from an ensemble of 20 networks on the cropped face, significantly outperforming the human reference. Expand
Conditional Probability Models for Deep Image Compression
TLDR
This paper proposes a new technique to navigate the rate-distortion trade-off for an image compression auto-encoder by using a context model: A 3D-CNN which learns a conditional probability model of the latent distribution of the auto- Encoder. Expand
NTIRE 2017 Challenge on Single Image Super-Resolution: Methods and Results
TLDR
This paper reviews the first challenge on single image super-resolution (restoration of rich details in an low resolution image) with focus on proposed solutions and results and gauges the state-of-the-art in single imagesuper-resolution. Expand
Pedestrian detection at 100 frames per second
We present a new pedestrian detector that improves both in speed and quality over state-of-the-art. By efficiently handling different scales and transferring computation from test time to trainingExpand
O-HAZE: A Dehazing Benchmark with Real Hazy and Haze-Free Outdoor Images
TLDR
The first outdoor scenes database composed of pairs of real hazy and corresponding haze-free images is introduced, and is used to compare a representative set of state-of-the-art dehazing techniques, using traditional image quality metrics such as PSNR, SSIM and CIEDE2000. Expand
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