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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

TIQA: Human-Aligned Perceptual Text Quality Assessment in Generated Images

Kirill Koltsov, Aleksandr Gushchin, Anastasia Antsiferova +1

Recent text-to-image models have improved global realism, but text rendering remains a persistent failure mode: images may look convincing overall, yet local typography often conta…

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…

cs.CV2025

Guardians of Image Quality: Benchmarking Defenses Against Adversarial Attacks on Image Quality Metrics

Alexander Gushchin, Khaled Abud, Georgii Bychkov +7

In the field of Image Quality Assessment (IQA), the adversarial robustness of the metrics poses a critical concern. This paper presents a comprehensive benchmarking study of variou…

cs.CV2025

FS-IQA: Certified Feature Smoothing for Robust Image Quality Assessment

Ekaterina Shumitskaya, Dmitriy Vatolin, Anastasia Antsiferova

We propose a novel certified defense method for Image Quality Assessment (IQA) models based on randomized smoothing with noise applied in the feature space rather than the input sp…