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Whole-cell Super-Resolution Imaging via DNA-PAINT on a Spinning Disk Confocal with Optical Photon Reassignment

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One Step Diffusion-Based Super-Resolution With Time-Aware Distillation.

Xiao He, Haoao Tang, Zhijun Tu

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|March 16, 2026

Summary

This summary is machine-generated.

TAD-SR accelerates diffusion-based image super-resolution (SR) using time-aware knowledge distillation. This novel framework enhances detail recovery and matches state-of-the-art performance with reduced inference time.

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Area of Science:

Computer Vision
Artificial Intelligence
Machine Learning

Background:

Diffusion models excel at image super-resolution (SR) but suffer from slow inference due to many sampling steps.
Existing knowledge distillation methods for SR often use pixel-level loss and ignore temporal information within diffusion models.

Purpose of the Study:

To develop a time-aware diffusion distillation framework (TAD-SR) for accelerating image super-resolution.
To improve the efficiency and detail recovery of diffusion-based SR models.

Main Methods:

Proposed a novel score distillation strategy to align student and teacher model score functions, reducing bias and focusing on high-frequency details.
Introduced a time-aware discriminator with temporal conditioning to leverage teacher knowledge across noise scales.
Implemented TAD-SR for efficient single-step diffusion-based SR.

Main Results:

TAD-SR significantly outperforms existing single-step diffusion SR methods.
The proposed method achieves performance comparable to multi-step state-of-the-art SR models.
Demonstrated effective detail recovery and accelerated inference.

Conclusions:

TAD-SR offers an efficient and effective approach to diffusion-based image super-resolution.
The time-aware distillation strategy is crucial for improving SR performance and reducing latency.
This framework advances the practical application of diffusion models in SR tasks.