Publications

2025

1. CVPR 2026

   

   Beyond the Ground Truth: Enhanced Supervision for Image Restoration

   Donghun Ryou^*, Inju Ha^*, Sanghyeok Chu, and Bohyung Han

   arXiv preprint, Dec 2025

   Accepted to CVPR 2026

   Abs project page arXiv Code

   Deep learning-based image restoration has achieved significant success. However, when addressing real-world degradations, model performance is limited by the quality of ground-truth images in datasets due to practical constraints in data acquisition. To address this limitation, we propose a novel framework that enhances existing ground truth images to provide higher-quality supervision for real-world restoration. Our framework generates perceptually enhanced ground truth images using super-resolution by incorporating adaptive frequency masks, which are learned by a conditional frequency mask generator. These masks guide the optimal fusion of frequency components from the original ground truth and its super-resolved variants, yielding enhanced ground truth images. This frequency-domain mixup preserves the semantic consistency of the original content while selectively enriching perceptual details, preventing hallucinated artifacts that could compromise fidelity. The enhanced ground truth images are used to train a lightweight output refinement network that can be seamlessly integrated with existing restoration models. Extensive experiments demonstrate that our approach consistently improves the quality of restored images. We further validate the effectiveness of both supervision enhancement and output refinement through user studies.

   TL;DR: We present a supervision enhancement framework that generates high-fidelity ground truths by fusing suboptimal references with super-resolved details via frequency-domain mixup.

2024

1. CVPRW 2025

   

   Learning to Translate Noise for Robust Image Denoising

   Inju Ha^*, Donghun Ryou^*, Seonguk Seo, and Bohyung Han

   arXiv preprint, Dec 2024

   DG-EBF Workshop, CVPR 2025

   Abs project page arXiv Code

   Image denoising techniques based on deep learning often struggle with poor generalization performance to out-of-distribution real-world noise. To tackle this challenge, we propose a novel noise translation framework that performs denoising on an image with translated noise rather than directly denoising an original noisy image. Specifically, our approach translates complex, unknown real-world noise into Gaussian noise, which is spatially uncorrelated and independent of image content, through a noise translation network. The translated noisy images are then processed by an image denoising network pretrained to effectively remove Gaussian noise, enabling robust and consistent denoising performance. We also design well-motivated loss functions and architectures for the noise translation network by leveraging the mathematical properties of Gaussian noise. Experimental results demonstrate that the proposed method substantially improves robustness and generalizability, outperforming state-of-the-art methods across diverse benchmarks.

   TL;DR: We present a robust denoising framework that simplifies the removal of complex real-world noise by translating it into Gaussian noise.
2. CVPR 2024

   

   Robust Image Denoising through Adversarial Frequency Mixup

   Donghun Ryou, Inju Ha, Hyewon Yoo, Dongwan Kim, and Bohyung Han

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Jun 2024

   Abs paper Supp Code

   Image denoising approaches based on deep neural networks often struggle with overfitting to specific noise distributions present in training data. This challenge persists in existing real-world denoising networks, which are trained using a limited spectrum of real noise distributions, and thus, show poor robustness to out-of-distribution real noise types. To alleviate this issue, we develop a novel training framework called Adversarial Frequency Mixup (AFM). AFM leverages mixup in the frequency domain to generate noisy images with distinctive and challenging noise characteristics, all the while preserving the properties of authentic real-world noise. Subsequently, incorporating these noisy images into the training pipeline enhances the denoising network’s robustness to variations in noise distributions. Extensive experiments and analyses, conducted on a wide range of real noise benchmarks demonstrate that denoising networks trained with our proposed framework exhibit significant improvements in robustness to unseen noise distributions.

   TL;DR: We present Adversarial Frequency Mixup (AFM), a novel training framework that enhances image denoising networks’ robustness to out-of-distribution noise.