activity
20172024
most citedControlling Information Capacity of Binary Neural Network

31 citations · 115 across the 19 of their papers we have counts for

collaborators
Showing 2022Show all

7 papers · 1 filter

cs.CV2022

MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning

Andrey Ignatov, Anastasia Sycheva, Radu Timofte +8

While neural networks-based photo processing solutions can provide a better image quality compared to the traditional ISP systems, their application to mobile devices is still very…

cs.CV2022

PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks

Andrey Ignatov, Grigory Malivenko, Radu Timofte +8

The increased importance of mobile photography created a need for fast and performant RAW image processing pipelines capable of producing good visual results in spite of the mobile…

eess.IV2022

Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report

Andrey Ignatov, Radu Timofte, Jin Zhang +21

As mobile cameras with compact optics are unable to produce a strong bokeh effect, lots of interest is now devoted to deep learning-based solutions for this task. In this Mobile AI…

eess.IV2022

Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report

Andrey Ignatov, Radu Timofte, Maurizio Denna +93

Image super-resolution is a common task on mobile and IoT devices, where one often needs to upscale and enhance low-resolution images and video frames. While numerous solutions hav…

eess.IV2022

Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report

Andrey Ignatov, Radu Timofte, Cheng-Ming Chiang +50

Video super-resolution is one of the most popular tasks on mobile devices, being widely used for an automatic improvement of low-bitrate and low-resolution video streams. While num…

cs.CV2022

Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report

Andrey Ignatov, Grigory Malivenko, Radu Timofte +36

Various depth estimation models are now widely used on many mobile and IoT devices for image segmentation, bokeh effect rendering, object tracking and many other mobile tasks. Thus…

cs.CV2022

Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report

Andrey Ignatov, Radu Timofte, Shuai Liu +35

The role of mobile cameras increased dramatically over the past few years, leading to more and more research in automatic image quality enhancement and RAW photo processing. In thi…