activity
20182020
most citedAIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

20 citations · 37 across the 2 of their papers we have counts for

collaborators

7 papers

cs.CV202020 cited

AIM 2020 Challenge on Real Image Super-Resolution: Methods and Results

Pengxu Wei, Hannan Lu, Radu Timofte +68

This paper introduces the real image Super-Resolution (SR) challenge that was part of the Advances in Image Manipulation (AIM) workshop, held in conjunction with ECCV 2020. This ch…

eess.IV2020

CycleISP: Real Image Restoration via Improved Data Synthesis

Syed Waqas Zamir, Aditya Arora, Salman Khan +4

The availability of large-scale datasets has helped unleash the true potential of deep convolutional neural networks (CNNs). However, for the single-image denoising problem, captur…

cs.CV2020

Learning Enriched Features for Real Image Restoration and Enhancement

Syed Waqas Zamir, Aditya Arora, Salman Khan +4

With the goal of recovering high-quality image content from its degraded version, image restoration enjoys numerous applications, such as in surveillance, computational photography…

cs.CV2019

AnimalWeb: A Large-Scale Hierarchical Dataset of Annotated Animal Faces

Muhammad Haris Khan, John McDonagh, Salman Khan +5

Being heavily reliant on animals, it is our ethical obligation to improve their well-being by understanding their needs. Several studies show that animal needs are often expressed…

cs.CV2019

iSAID: A Large-scale Dataset for Instance Segmentation in Aerial Images

Syed Waqas Zamir, Aditya Arora, Akshita Gupta +7

Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. In this work, we introduce the first benchmark dataset for instance segmentation i…

cs.CV201917 cited

Learning Digital Camera Pipeline for Extreme Low-Light Imaging

Syed Waqas Zamir, Aditya Arora, Salman Khan +2

In low-light conditions, a conventional camera imaging pipeline produces sub-optimal images that are usually dark and noisy due to a low photon count and low signal-to-noise ratio…