Masked Image Training for Generalizable Deep Image Denoising

Chen, Haoyu; Gu, Jinjin; Liu, Yihao; Magid, Salma Abdel; Dong, Chao; Wang, Qiong; Pfister, Hanspeter; Zhu, Lei

doi:10.1109/cvpr52729.2023.00169

Cited by 25 publications

(1 citation statement)

References 61 publications

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“…Low-dose CT image denoising uses images from the 2016 NIH-AAPM-Mayo Clinic Low-Dose CT Grand Challenge (LDCT) [46], with regular-dose CT images as a reference from the same dataset. Eight models in total were included in the evaluation: CNN-based models (DnCNN, FFDNet, DRUNet), Transformerbased methods (SwinIR, Restormer), masked training (MT) [47], traditional methods like BM3D, and the unsupervised DIP model. The training of the MPFDIP denoising model consisted of two phases.…”

Section: Datasets and Experimental Setupmentioning

confidence: 99%

A Masked-Pre-Training-Based Fast Deep Image Prior Denoising Model

Ji,

Xu,

Cheng

et al. 2024

Applied Sciences

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Compared to supervised denoising models based on deep learning, the unsupervised Deep Image Prior (DIP) denoising approach offers greater flexibility and practicality by operating solely with the given noisy image. However, the random initialization of network input and network parameters in the DIP leads to a slow convergence during iterative training, affecting the execution efficiency heavily. To address this issue, we propose the Masked-Pre-training-Based Fast DIP (MPFDIP) Denoising Model in this paper. We enhance the classical Restormer framework by improving its Transformer core module and incorporating sampling, residual learning, and refinement techniques. This results in a fast network called FRformer (Fast Restormer). The FRformer model undergoes offline supervised training using the masked processing technique for pre-training. For a specific noisy image, the pre-trained FRformer network, with its learned parameters, replaces the UNet network used in the original DIP model. The online iterative training of the replaced model follows the DIP unsupervised training approach, utilizing multi-target images and an adaptive loss function. This strategy further improves the denoising effectiveness of the pre-trained model. Extensive experiments demonstrate that the MPFDIP model outperforms existing mainstream deep-learning-based denoising models in reducing Gaussian noise, mixed Gaussian–Poisson noise, and low-dose CT noise. It also significantly enhances the execution efficiency compared to the original DIP model. This improvement is mainly attributed to the FRformer network’s initialization parameters obtained through masked pre-training, which exhibit strong generalization capabilities for various types and intensities of noise and already provide some denoising effect. Using them as initialization parameters greatly improves the convergence speed of unsupervised iterative training in the DIP. Additionally, the techniques of multi-target images and the adaptive loss function further enhance the denoising process.

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Section: Datasets and Experimental Setupmentioning

confidence: 99%