QualiTeacher: Quality-Conditioned Pseudo-Labeling for Real-World Image Restoration
Authors: Fengyang Xiao, Jingjia Feng, Peng Hu, Dingming Zhang, Lei Xu, Guanyi Qin, Lu Li, Chunming He, Sina Farsiu
Organizations: Duke University · Tsinghua University · École Polytechnique Fédérale de Lausanne (EPFL) · National University of Singapore · Sun Yat-sen University
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 & tables
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.
Desnowing
Methods
CNNIQA ↑
HyperIQA ↑
ManIQA ↑
Topiq ↑
Arniqa ↑
Q-Align ↑
C2score ↑
LIQE ↑
SemiDDM-Weather [ 30 ]
0.636
0.423
0.342
0.442
0.567
3.586
55.068
2.497
SnowMaster (NAFNet) [ 19 ]
0.670
0.501
0.349
0.499
0.612
3.836
55.327
2.768
NAFNet + Colabator [ 10 ]
0.628
0.470
0.342
0.473
0.620
3.936
55.389
2.667
NAFNet + QualiTeacher
0.675
0.559
0.362
0.540
0.628
4.030
55.446
2.883
HistoFormer [ 34 ]
0.615
0.444
0.346
0.455
0.585
3.827
55.266
2.610
Table 1: NR-IQA comparisons on Desnowing, Deraining, Dehazing, LLIE, and UIE.
Table 6Figure 7
Methods
CNNIQA ↑
ManIQA ↑
Topiq ↑
Arniqa ↑
Q-Align ↑
C2score ↑
LIQE ↑
MFRQA ↑
CIDNet [ 46 ]
0.554
0.354
0.480
0.618
3.715
54.944
3.049
58.860
CIDNet + Colabator [ 10 ]
0.532
0.367
0.439
0.609
3.571
54.967
2.929
59.319
CIDNet + QualiTeacher1
0.660
0.432
0.530
0.661
3.643
55.454
3.411
59.675
CIDNet + QualiTeacher(Ours)
0.669
0.428
0.542
0.657
3.741
55.400
3.322
59.341
Table 4: Ablation on the IQA selection. QualiTeacher with alternative NR-IQA metrics (MANIQA, TOPIQ, QualiClip) on LLIE.
Methods (AP)
Bicycle
Boat
Bottle
Bus
Car
Cat
Chair
Cup
Dog
Motor
People
Table
Mean
DRBN [ 49 ]
81.8
81.1
73.5
90.2
79.6
72.6
65.1
69.9
78.2
71.2
76.7
62.7
75.2
LMT-GP(SemiLL) [ 50 ]
80.9
82.7
76.2
91.2
80.2
75.1
72.1
70.6
77.8
71.1
78.8
65.8
76.9
SemiLL + Colabator [ 10 ]
79.6
82.0
75.7
89.2
80.8
69.5
70.0
70.8
79.9
71.6
76.8
65.6
76.0
CIDNet + Colabator [ 10 ]
80.7
79.8
76.0
86.6
81.2
75.2
71.9
71.9
79.6
72.1
78.3
67.3
76.7
SemiLL + QualiTeacher
82.6
83.3
78.2
91.2
82.1
70.9
74.6
72.0
80.7
74.2
80.5
67.9
78.2
CIDNet + QualiTeacher
82.9
83.0
76.4
92.5
82.5
77.8
73.3
73.5
82.8
74.0
81.4
68.7
79.1
Table 5: Low-light image detection on ExDark .
Methods (Task: Desnowing)
CNNIQA ↑
HyperIQA ↑
ManIQA ↑
Topiq ↑
Arniqa ↑
Q-Align ↑
C2score ↑
LIQE ↑
SnowMaster(NAFNet) [ 19 ]
0.670
0.501
0.349
0.499
0.612
3.836
55.327
2.768
NAFNet + Colabator [ 10 ]
0.628
0.470
0.342
0.473
0.620
3.936
55.389
2.667
NAFNet + QualiTeacher(Topiq)
0.671
0.538
0.361
0.540
0.614
3.999
55.398
2.826
NAFNet + QualiTeacher
0.675
0.559
0.362
0.540
0.628
4.030
55.446
2.883
Table 6: Robust analysis for NR-IQA metrics. We replace MUSIQ with TOPIQ.