31 citations · 115 across the 19 of their papers we have counts for
13 papers · 1 filter
Virtually Enriched NYU Depth V2 Dataset for Monocular Depth Estimation: Do We Need Artificial Augmentation?
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. In contrast to the well-known approach where full 3D scenes…
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…
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…
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…
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…
AIM 2020 Challenge on Learned Image Signal Processing Pipeline
Andrey Ignatov, Radu Timofte, Zhilu Zhang +36
This paper reviews the second AIM learned ISP challenge and provides the description of the proposed solutions and results. The participating teams were solving a real-world RAW-to…
Controlling Information Capacity of Binary Neural Network
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. Whi…
Rendering Natural Camera Bokeh Effect with Deep Learning
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. While DSLR and system camera lenses can render…
Replacing Mobile Camera ISP with a Single Deep Learning Model
Andrey Ignatov, Luc Van Gool, Radu Timofte
As the popularity of mobile photography is growing constantly, lots of efforts are being invested now into building complex hand-crafted camera ISP solutions. In this work, we demo…
Fast Perceptual Image Enhancement
Etienne de Stoutz, Andrey Ignatov, Nikolay Kobyshev +2
The vast majority of photos taken today are by mobile phones. While their quality is rapidly growing, due to physical limitations and cost constraints, mobile phone cameras struggl…
PIRM Challenge on Perceptual Image Enhancement on Smartphones: Report
Andrey Ignatov, Radu Timofte, Thang Van Vu +45
This paper reviews the first challenge on efficient perceptual image enhancement with the focus on deploying deep learning models on smartphones. The challenge consisted of two tra…
WESPE: Weakly Supervised Photo Enhancer for Digital Cameras
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte +2
Low-end and compact mobile cameras demonstrate limited photo quality mainly due to space, hardware and budget constraints. In this work, we propose a deep learning solution that tr…
DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks
Andrey Ignatov, Nikolay Kobyshev, Radu Timofte +2
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…