Median smoothing plus a trained denoiser with ranking loss yields certified l2 robustness for no-reference image quality metrics while preserving correlation with subjective scores better than prior smoothing baselines.
Can No-Reference Quality-Assessment Methods Serve as Perceptual Losses for Super-Resolution?
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
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 perceptual losses is often limited to LPIPS, a fullreference method. Even though deep no-reference image-qualityassessment methods are excellent at predicting human judgment, little research has examined their incorporation in loss functions. This paper investigates direct optimization of several video-superresolution models using no-reference image-quality-assessment methods as perceptual losses. Our experimental results show that straightforward optimization of these methods produce artifacts, but a special training procedure can mitigate them.
fields
eess.IV 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment
Median smoothing plus a trained denoiser with ranking loss yields certified l2 robustness for no-reference image quality metrics while preserving correlation with subjective scores better than prior smoothing baselines.