collaborators

5 papers

cs.CV2026

SR-Ground: Image Quality Grounding for Super-Resolved Content

Artem Borisov, Evgeney Bogatyrev, Khaled Abud +1

Super-Resolution (SR) has advanced rapidly in recent years, with diffusion-based models achieving unprecedented fidelity at the cost of introducing new types of visual artifacts. W…

cs.CV2026

SR-Prominence: A Crowdsourced Protocol and Dataset Suite for Perceptually-Weighted Super-Resolution Artifact Evaluation

Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev +3

Modern image super-resolution methods generate detailed, visually appealing results, but they often introduce visual artifacts: unnatural patterns and texture distortions that degr…

cs.CV2026

Exploring Real-Time Super-Resolution: Benchmarking and Fine-Tuning for Streaming Content

Evgeney Bogatyrev, Khaled Abud, Ivan Molodetskikh +2

Recent advancements in real-time super-resolution have enabled higher-quality video streaming, yet existing methods struggle with the unique challenges of compressed video content.…

cs.CV2026

Prominence-Aware Artifact Detection and Dataset for Image Super-Resolution

Ivan Molodetskikh, Kirill Malyshev, Mark Mirgaleev +3

Generative single-image super-resolution (SISR) is advancing rapidly, yet even state-of-the-art models produce visual artifacts: unnatural patterns and texture distortions that deg…

eess.IV2024

SR+Codec: a Benchmark of Super-Resolution for Video Compression Bitrate Reduction

Evgeney Bogatyrev, Ivan Molodetskikh, Dmitriy Vatolin

In recent years, there has been significant interest in Super-Resolution (SR), which focuses on generating a high-resolution image from a low-resolution input. Deep learning-based…