I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images

• URL: http://arxiv.org/abs/2302.10523v1
• Date: Tue, 21 Feb 2023 08:51:17 GMT
• Title: I2V: Towards Texture-Aware Self-Supervised Blind Denoising using Self-Residual Learning for Real-World Images
• Authors: Kanggeun Lee, Kyungryun Lee, and Won-Ki Jeong
• Abstract summary: pixel-shuffle downsampling (PD) has been proposed to eliminate the spatial correlation of noise. We propose self-residual learning without the PD process to maintain texture information. The results of extensive experiments show that the proposed method outperforms state-of-the-art self-supervised blind denoising approaches.
• Score: 8.763680382529412
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Although the advances of self-supervised blind denoising are significantly superior to conventional approaches without clean supervision in synthetic noise scenarios, it shows poor quality in real-world images due to spatially correlated noise corruption. Recently, pixel-shuffle downsampling (PD) has been proposed to eliminate the spatial correlation of noise. A study combining a blind spot network (BSN) and asymmetric PD (AP) successfully demonstrated that self-supervised blind denoising is applicable to real-world noisy images. However, PD-based inference may degrade texture details in the testing phase because high-frequency details (e.g., edges) are destroyed in the downsampled images. To avoid such an issue, we propose self-residual learning without the PD process to maintain texture information. We also propose an order-variant PD constraint, noise prior loss, and an efficient inference scheme (progressive random-replacing refinement ($\text{PR}^3$)) to boost overall performance. The results of extensive experiments show that the proposed method outperforms state-of-the-art self-supervised blind denoising approaches, including several supervised learning methods, in terms of PSNR, SSIM, LPIPS, and DISTS in real-world sRGB images.

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