3 papers
cs.CV2025
MEMFOF: High-Resolution Training for Memory-Efficient Multi-Frame Optical Flow Estimation
Vladislav Bargatin, Egor Chistov, Alexander Yakovenko +1
Recent advances in optical flow estimation have prioritized accuracy at the cost of growing GPU memory consumption, particularly for high-resolution (FullHD) inputs. We introduce M…
eess.IV2024
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
Egor Kashkarov, Egor Chistov, Ivan Molodetskikh +1
Perceptual losses play an important role in constructing deep-neural-network-based methods by increasing the naturalness and realism of processed images and videos. Use of perceptu…
cs.CV2023
BASED: Benchmarking, Analysis, and Structural Estimation of Deblurring
Nikita Alutis, Egor Chistov, Mikhail Dremin +1
This paper discusses the challenges of evaluating deblurring-methods quality and proposes a reduced-reference metric based on machine learning. Traditional quality-assessment metri…