Search Results for author: Andrey Ignatov

Found 20 papers, 10 papers with code

Fast and Accurate Camera Scene Detection on Smartphones

no code implementations17 May 2021 Angeline Pouget, Sidharth Ramesh, Maximilian Giang, Ramithan Chandrapalan, Toni Tanner, Moritz Prussing, Radu Timofte, Andrey Ignatov

AI-powered automatic camera scene detection mode is nowadays available in nearly any modern smartphone, though the problem of accurate scene prediction has not yet been addressed by the research community.

Controlling Information Capacity of Binary Neural Network

no code implementations4 Aug 2020 Dmitry Ignatov, Andrey Ignatov

Despite the growing popularity of deep learning technologies, high memory requirements and power consumption are essentially limiting their application in mobile and IoT areas.

Rendering Natural Camera Bokeh Effect with Deep Learning

1 code implementation10 Jun 2020 Andrey Ignatov, Jagruti Patel, Radu Timofte

Bokeh is an important artistic effect used to highlight the main object of interest on the photo by blurring all out-of-focus areas.

Replacing Mobile Camera ISP with a Single Deep Learning Model

2 code implementations13 Feb 2020 Andrey Ignatov, Luc van Gool, Radu Timofte

The model is trained to convert RAW Bayer data obtained directly from mobile camera sensor into photos captured with a professional high-end DSLR camera, making the solution independent of any particular mobile ISP implementation.

Demosaicking Denoising

AI Benchmark: All About Deep Learning on Smartphones in 2019

no code implementations15 Oct 2019 Andrey Ignatov, Radu Timofte, Andrei Kulik, Seungsoo Yang, Ke Wang, Felix Baum, Max Wu, Lirong Xu, Luc van Gool

The performance of mobile AI accelerators has been evolving rapidly in the past two years, nearly doubling with each new generation of SoCs.

Fast Perceptual Image Enhancement

1 code implementation31 Dec 2018 Etienne de Stoutz, Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Luc van Gool

We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras.

Image Enhancement

AI Benchmark: Running Deep Neural Networks on Android Smartphones

1 code implementation2 Oct 2018 Andrey Ignatov, Radu Timofte, William Chou, Ke Wang, Max Wu, Tim Hartley, Luc van Gool

Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago.

WESPE: Weakly Supervised Photo Enhancer for Digital Cameras

3 code implementations4 Sep 2017 Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, Luc van Gool

Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints.

A Large-Scale CNN Ensemble for Medication Safety Analysis

no code implementations17 Jun 2017 Liliya Akhtyamova, Andrey Ignatov, John Cardiff

Revealing Adverse Drug Reactions (ADR) is an essential part of post-marketing drug surveillance, and data from health-related forums and medical communities can be of a great significance for estimating such effects.

General Classification

Decision Stream: Cultivating Deep Decision Trees

1 code implementation25 Apr 2017 Dmitry Ignatov, Andrey Ignatov

Various modifications of decision trees have been extensively used during the past years due to their high efficiency and interpretability.

Classification Feature Selection +3

DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks

3 code implementations ICCV 2017 Andrey Ignatov, Nikolay Kobyshev, Radu Timofte, Kenneth Vanhoey, Luc van Gool

Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations - small sensor size, compact lenses and the lack of specific hardware, - impede them to achieve the quality results of DSLR cameras.


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