Georgy Perevozchikov

ORCID: 0009-0009-7176-6242
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About
Contact & Profiles
Research Areas
  • Advanced Vision and Imaging
  • Image Enhancement Techniques
  • Advanced Image Processing Techniques
  • Advanced Image and Video Retrieval Techniques
  • Image and Video Quality Assessment
  • Video Coding and Compression Technologies
  • Advanced Neural Network Applications
  • Digital Rights Management and Security
  • Industrial Vision Systems and Defect Detection
  • Image Processing Techniques and Applications

Moscow Power Engineering Institute
2024

Institute for Information Transmission Problems
2024

Moscow Institute of Physics and Technology
2023-2024

This paper presents a review of the NTIRE 2023 challenge on night photography rendering.The goal was to find solutions that process raw camera images taken in nighttime conditions conditions, and thereby produce photo-quality output standard RGB (sRGB) space.Unlike previous year's competition, participants were not provided with large training dataset for target sensor.Instead, this time they given color checker illuminated by known light source.To evaluate results, sufficient number viewers...

10.1109/cvprw59228.2023.00192 article EN 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) 2023-06-01

Modern smartphone camera quality heavily relies on the image signal processor (ISP) to enhance captured raw images, utilizing carefully designed modules produce final output images encoded in a standard color space (e.g., sRGB). Neural-based end-to-end learnable ISPs offer promising advancements, potentially replacing traditional with their ability adapt without requiring extensive tuning for each new model, as is often case nearly every module ISPs. However, key challenge recent...

10.48550/arxiv.2404.10700 preprint EN arXiv (Cornell University) 2024-04-16

10.5220/0012268900003660 article EN Proceedings of the 17th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications 2024-01-01

The role of mobile cameras increased dramatically over the past few years, leading to more and research in automatic image quality enhancement RAW photo processing. In this Mobile AI challenge, target was develop an efficient end-to-end AI-based signal processing (ISP) pipeline replacing standard ISPs that can run on modern smartphone GPUs using TensorFlow Lite. participants were provided with a large-scale Fujifilm UltraISP dataset consisting thousands paired photos captured normal camera...

10.48550/arxiv.2211.03885 preprint EN other-oa arXiv (Cornell University) 2022-01-01
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