2 citations · 2 across the 4 of their papers we have counts for
6 papers
DISRQAD: Diffusion Image Super-Resolution Quality Assessment Dataset and Benchmark
Nikita Kukuzei, Artem Borisov, Evgeney Bogatyrev +3
Diffusion-based image super-resolution (SR) can create visually plausible detail that is not supported by the low-resolution input. We introduce DISRQAD, a subjective-quality datas…
TSRN-RTVD: Real-Time Video Deblurring System
Nikita Alutis, Danila Evsyukov, Egor Chistov +3
As video capture moves to handheld and edge devices, motion blur from camera shake has become a pervasive degradation that lowers perceptual quality and harms downstream vision tas…
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…
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…
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…
Color Mismatches in Stereoscopic Video: Real-World Dataset and Deep Correction Method
Egor Chistov, Nikita Alutis, Dmitriy Vatolin
Stereoscopic videos can contain color mismatches between the left and right views due to minor variations in camera settings, lenses, and even object reflections captured from diff…