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

Real-world image restoration (RWIR) is a highly challeng-ing task due to the absence of clean ground-truth images. Many recentmethods resort to pseudo-label (PL) supervision, often within a Mean-Teacher (MT) framework, where a teacher network generates targets fora student network. However, these methods face a critical paradox: un-conditionally trusting the often imperfect, low-quality PLs forces the stu-dent model to learn undesirable artifacts, while discarding them severelylimits data diversity and impairs model generalization. In this paper, wepropose QualiTeacher, a novel framework that transforms pseudo-labelquality from a noisy liability into a conditional supervisory signal. In-stead of filtering, QualiTeacher explicitly conditions the student modelon the quality of the PLs, estimated by an ensemble of complementarynon-reference image quality assessment (NR-IQA) models spanning low-level distortion and semantic-level assessment. This strategy teaches thestudent network to learn a quality-graded restoration manifold, enablingit to understand what constitutes different quality levels. Consequently,it can not only avoid mimicking artifacts from low-quality labels but alsoextrapolate 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 di-versify the PL quality spectrum, a score-based preference optimizationstrategy inspired by Direct Preference Optimization (DPO) to enforcea monotonically ordered quality separation, and a cropped consistencyloss to prevent adversarial over-optimization (reward hacking) of theIQA models. Experiments on standard RWIR benchmarks demonstratethat QualiTeacher can serve as a plug-and-play strategy to improve thequality of the existing pseudo-labeling framework, establishing a newparadigm for learning from imperfect supervision. Code will be releasedat https://github.com/fengyang1399-pixel/QualiTeacher.git .6.87.52 F. Xiao et al.(a) Unconditional Trust (b) Aggressive Filtering (c) QualiTeacher5.2 6.8 7.5 5.2 6.8 7.5Aug AugTeacher Teacher NR-IQA Teacher NR-IQAInput PL Input Optimal PL Input Quality-Conditioned PLsArtifacts are replicated rather than removed Limited data diversity, over-smoothed and blurry Better generalization via quality-conditioned pseudo-labelsDegraded Unconditional Trust Aggressive Filtering QualiTeacherFig. 1: Comparison of pseudo-label (PL) utilization strategies in the mean-teacherframework. (a) Unconditional Trust: PLs are used without filtering, causing degra-dation artifacts to be replicated by the student. (b) Aggressive Filtering: Low-quality PLs are discarded via NR-IQA filtering, yet over-smoothed outputs that receivedeceptively high scores survive, introducing blurriness. (c) QualiTeacher (Ours):Nearly all PLs are retained (with only extreme outliers discarded), with their qualityscores injected as continuous conditioning signals, enabling the student to leverage fulldata diversity while remaining aware of each sample’s reliability.