activity
20172022
most citedLossless Compression of Mosaic Images with Convolutional Neural Network Prediction

3 citations · 10 across the 9 of their papers we have counts for

collaborators
Showing cs.CVShow all

8 papers · 1 filter

cs.CV20212 cited

Light Pollution Reduction in Nighttime Photography

Chang Liu, Xiaolin Wu

Nighttime photographers are often troubled by light pollution of unwanted artificial lights. Artificial lights, after scattered by aerosols in the atmosphere, can inundate the star…

cs.CV20211 cited

Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton

Xi Zhang, Xiaolin Wu

We propose a deep learning system for attention-guided dual-layer image compression (AGDL). In the AGDL compression system, an image is encoded into two layers, a base layer and an…

cs.CV20201 cited

Adaptive Loss Function for Super Resolution Neural Networks Using Convex Optimization Techniques

Seyed Mehdi Ayyoubzadeh, Xiaolin Wu

Single Image Super-Resolution (SISR) task refers to learn a mapping from low-resolution images to the corresponding high-resolution ones. This task is known to be extremely difficu…

cs.CV2019

Challenge of Spatial Cognition for Deep Learning

Xi Zhang, Xiaolin Wu, Jun Du

Given the success of the deep convolutional neural networks (DCNNs) in applications of visual recognition and classification, it would be tantalizing to test if DCNNs can also lear…

cs.CV2018

Deep Learning with Inaccurate Training Data for Image Restoration

Bolin Liu, Xiao Shu, Xiaolin Wu

In many applications of deep learning, particularly those in image restoration, it is either very difficult, prohibitively expensive, or outright impossible to obtain paired traini…

cs.CV2018

Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination

Chang Liu, Xiaolin Wu, Xiao Shu

All existing image enhancement methods, such as HDR tone mapping, cannot recover A/D quantization losses due to insufficient or excessive lighting, (underflow and overflow problems…