Search Results for author: Andrey Ignatov

Found 31 papers, 18 papers with code

Histopathological Image Classification with Cell Morphology Aware Deep Neural Networks

1 code implementation11 Jul 2024 Andrey Ignatov, Josephine Yates, Valentina Boeva

To deal with this problem, we propose a novel DeepCMorph model pre-trained to learn cell morphology and identify a large number of different cancer types.

Histopathological Image Classification Image Classification

Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?

1 code implementation15 Apr 2024 Dmitry Ignatov, Andrey Ignatov, Radu Timofte

We present ANYU, a new virtually augmented version of the NYU depth v2 dataset, designed for monocular depth estimation.

Monocular Depth Estimation

Real-World Mobile Image Denoising Dataset with Efficient Baselines

1 code implementation CVPR 2024 Roman Flepp, Andrey Ignatov, Radu Timofte, Luc van Gool

Despite the latest advancements in camera hardware the mobile camera sensor area cannot be increased significantly due to physical constraints leading to a pixel size of 0. 6--2. 0 \mum which results in strong image noise even in moderate lighting conditions.

Image Denoising

SQAD: Automatic Smartphone Camera Quality Assessment and Benchmarking

1 code implementation ICCV 2023 Zilin Fang, Andrey Ignatov, Eduard Zamfir, Radu Timofte

Smartphone photography is becoming increasingly popular, but fitting high-performing camera systems within the given space limitations remains a challenge for manufacturers.


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

no code implementations8 Nov 2022 Andrey Ignatov, Anastasia Sycheva, Radu Timofte, Yu Tseng, Yu-Syuan Xu, Po-Hsiang Yu, Cheng-Ming Chiang, Hsien-Kai Kuo, Min-Hung Chen, Chia-Ming Cheng, Luc van Gool

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 limited due to their very high computational complexity.

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

1 code implementation8 Nov 2022 Andrey Ignatov, Grigory Malivenko, Radu Timofte, Yu Tseng, Yu-Syuan Xu, Po-Hsiang Yu, Cheng-Ming Chiang, Hsien-Kai Kuo, Min-Hung Chen, Chia-Ming Cheng, Luc van Gool

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 camera sensor limitations.

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

2 code implementations7 Nov 2022 Andrey Ignatov, Radu Timofte, Maurizio Denna, Abdel Younes, Ganzorig Gankhuyag, Jingang Huh, Myeong Kyun Kim, Kihwan Yoon, Hyeon-Cheol Moon, Seungho Lee, Yoonsik Choe, Jinwoo Jeong, Sungjei Kim, Maciej Smyl, Tomasz Latkowski, Pawel Kubik, Michal Sokolski, Yujie Ma, Jiahao Chao, Zhou Zhou, Hongfan Gao, Zhengfeng Yang, Zhenbing Zeng, Zhengyang Zhuge, Chenghua Li, Dan Zhu, Mengdi Sun, Ran Duan, Yan Gao, Lingshun Kong, Long Sun, Xiang Li, Xingdong Zhang, Jiawei Zhang, Yaqi Wu, Jinshan Pan, Gaocheng Yu, Jin Zhang, Feng Zhang, Zhe Ma, Hongbin Wang, Hojin Cho, Steve Kim, Huaen Li, Yanbo Ma, Ziwei Luo, Youwei Li, Lei Yu, Zhihong Wen, Qi Wu, Haoqiang Fan, Shuaicheng Liu, Lize Zhang, Zhikai Zong, Jeremy Kwon, Junxi Zhang, Mengyuan Li, Nianxiang Fu, Guanchen Ding, Han Zhu, Zhenzhong Chen, Gen Li, Yuanfan Zhang, Lei Sun, Dafeng Zhang, Neo Yang, Fitz Liu, Jerry Zhao, Mustafa Ayazoglu, Bahri Batuhan Bilecen, Shota Hirose, Kasidis Arunruangsirilert, Luo Ao, Ho Chun Leung, Andrew Wei, Jie Liu, Qiang Liu, Dahai Yu, Ao Li, Lei Luo, Ce Zhu, Seongmin Hong, Dongwon Park, Joonhee Lee, Byeong Hyun Lee, Seunggyu Lee, Se Young Chun, Ruiyuan He, Xuhao Jiang, Haihang Ruan, Xinjian Zhang, Jing Liu, Garas Gendy, Nabil Sabor, Jingchao Hou, Guanghui He

While numerous solutions have been proposed for this problem in the past, they are usually not compatible with low-power mobile NPUs having many computational and memory constraints.

Image Super-Resolution

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

3 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.

Generative Adversarial Network

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 Marketing

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 +4

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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