QualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration

TL;DR

QualiTeacher is proposed, a novel framework that transforms pseudo-label quality from a noisy liability into a conditional supervisory signal and can serve as a plug-and-play strategy to improve the quality of the existing pseudo-labeling framework, establishing a new paradigm for learning from imperfect supervision.

Abstract

Real-world image restoration (RWIR) is a highly challenging task due to the absence of clean ground-truth images. Many recent methods resort to pseudo-label (PL) supervision, often within a Mean-Teacher (MT) framework. However, these methods face a critical paradox: unconditionally trusting the often imperfect, low-quality PLs forces the student model to learn undesirable artifacts, while discarding them severely limits data diversity and impairs model generalization. In this paper, we propose QualiTeacher, a novel framework that transforms pseudo-label quality from a noisy liability into a conditional supervisory signal. Instead of filtering, QualiTeacher explicitly conditions the student model on the quality of the PLs, estimated by an ensemble of complementary non-reference image quality assessment (NR-IQA) models spanning low-level distortion and semantic-level assessment. This strategy teaches the student network to learn a quality-graded restoration manifold, enabling it to understand what constitutes different quality levels. Consequently, it can not only avoid mimicking artifacts from low-quality labels but also extrapolate to generate results of higher quality than the teacher itself. To ensure the robustness and accuracy of this quality-driven learning, we further enhance the process with a multi-augmentation scheme to diversify the PL quality spectrum, a score-based preference optimization strategy inspired by Direct Preference Optimization (DPO) to enforce a monotonically ordered quality separation, and a cropped consistency loss to prevent adversarial over-optimization (reward hacking) of the IQA models. Experiments on standard RWIR benchmarks demonstrate that QualiTeacher can serve as a plug-and-play strategy to improve the quality of the existing pseudo-labeling framework, establishing a new paradigm for learning from imperfect supervision. Code will be released.

Figures (8)

• Figure 1: Comparison of pseudo-label (PL) utilization strategies in the mean-teacher framework. (a) Unconditional Trust: PLs are used without filtering, causing degradation artifacts to be replicated by the student. (b) Aggressive Filtering: Low-quality PLs are discarded via NR-IQA filtering, yet over-smoothed outputs that receive deceptively high scores survive, introducing blurriness. (c) QualiTeacher (Ours): Nearly all PLs are retained (with only extreme outliers discarded), with their quality scores injected as continuous conditioning signals, enabling the student to leverage full data diversity while remaining aware of each sample's reliability.
• Figure 2: Overall framework of QualiTeacher. By re-purposing pseudo-label quality as a conditional signal rather than a filtering criterion, the student network can learn a quality-graded manifold and extrapolate beyond the teacher's capabilities.
• Figure 3: Overview of the quality-driven optimization strategy in QualiTeacher. Left: Cropped Quality Consistency Loss. Right: Score-Based Preference Optimization.
• Figure 4: Visualizations on desnowing, deraining, dehazing, LLIE, and UIE.
• Figure 5: Visualization of breakdown ablation, where (a), (b), (c), (d), and (e) are consistent with those in \ref{['tab:ablation']}.