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Shuigen Wang

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3 papers

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AAAI Conference 2026 Conference Paper

Thermal-Physics Guided Infrared Image Super-Resolution with Dynamic High-Frequency Amplification

Mingxuan Zhou
Yirui Shen
Shuang Li
Jing Geng
Yutang Zhang
Shuigen Wang

The practical deployment of infrared imaging is hindered by its inherent output of low-resolution (LR) images. While the super-resolution (SR) technique is a promising remedy, we discover two major challenges concerning infrared image SR: preserving accurate thermal distributions, which are fundamental to infrared imaging, and addressing the ambiguity of high-frequency elements compared to visible images. To tackle these issues, we propose ThesIS, a tailored framework that utilizes Thermal-Physics guidance and dynamic high-frequency amplification for Infrared image Super-resolution to produce high-resolution (HR) images with accurate physical properties and delicate visual details. Specifically, Thermal Regularization is introduced to reconstruct the accurate thermal radiation distribution via the introduced Infrared Radiation Intensity Alignment Loss, mitigating the adverse effects of complex degradations while conducting initial upscaling. Additionally, we design a guidance mechanism to counter the randomness of the diffusion model, further refining the preservation of physical information. The proposed Dynamic High-Frequency Amplification effectively strengthens the ambiguous high-frequency information present in infrared images, leading to improved texture details and superior visual quality. Extensive experiments demonstrate that ThesIS successfully recovers accurate thermal information while delivering visually satisfying results with state-of-the-art performance. Furthermore, we introduce the InfraredSR dataset, which comprises 39,833 images at a resolution of 512 × 512, hoping to advance research in this field.

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AAAI Conference 2024 Conference Paper

Multi-Domain Multi-Scale Diffusion Model for Low-Light Image Enhancement

Kai Shang
Mingwen Shao
Chao Wang
Yuanshuo Cheng
Shuigen Wang

Diffusion models have achieved remarkable progress in low-light image enhancement. However, there remain two practical limitations: (1) existing methods mainly focus on the spatial domain for the diffusion process, while neglecting the essential features in the frequency domain; (2) conventional patch-based sampling strategy inevitably leads to severe checkerboard artifacts due to the uneven overlapping. To address these limitations in one go, we propose a Multi-Domain Multi-Scale (MDMS) diffusion model for low-light image enhancement. In particular, we introduce a spatial-frequency fusion module to seamlessly integrates spatial and frequency information. By leveraging the Multi-Domain Learning (MDL) paradigm, our proposed model is endowed with the capability to adaptively facilitate noise distribution learning, thereby enhancing the quality of the generated images. Meanwhile, we propose a Multi-Scale Sampling (MSS) strategy that follows a divide-ensemble manner by merging the restored patches under different resolutions. Such a multi-scale learning paradigm explicitly derives patch information from different granularities, thus leading to smoother boundaries. Furthermore, we empirically adopt the Bright Channel Prior (BCP) which indicates natural statistical regularity as an additional restoration guidance. Experimental results on LOL and LOLv2 datasets demonstrate that our method achieves state-of-the-art performance for the low-light image enhancement task. Codes are available at https://github.com/Oliiveralien/MDMS.

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EAAI Journal 2024 Journal Article

When guided diffusion model meets zero-shot image super-resolution

Huan Liu
Mingwen Shao
Kai Shang
Yuanjian Qiao
Shuigen Wang

Existing deep learning-based single-image super-resolution (SR) methods typically rely on vast quantities of paired data. As an essential solution, zero-shot SR methods require only a single image to handle image-specific degradation. However, these methods still struggle to recover fine-grained details due to the lack of supervised information. In this work, we propose a novel guided Diffusion model for Zero-shot image SR (ZeroDiff) to explicitly direct image quality enhancement. Specifically, we elaborate two key guidance strategies: (1) high-frequency guidance and (2) content-consistent guidance. The former concentrates on boosting fine-grained textures by embedding high-frequency information into the cross-attention mechanism of the noise estimator. The latter avoids the sampling deviating from the original image in terms of structure and low-frequency content. Specifically, the noisy images at each diffusion step are injected into the corresponding sampling step, encouraging the sampled image to be consistent with that of the corresponding diffusion step. Moreover, we design a progressive zoom-in paradigm by gradually enlarging the image size and enriching the image details to boost the sampling efficiency of diffusion models, while enabling high-quality image reconstruction. Extensive experiments reveal that our method achieves comparable results with other state-of-the-art methods in quantitative and qualitative evaluations on both face and natural images from synthetic and real-world datasets.

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