Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution

• URL: http://arxiv.org/abs/2303.05156v3
• Date: Thu, 13 Jul 2023 06:27:11 GMT
• Title: Local Implicit Normalizing Flow for Arbitrary-Scale Image Super-Resolution
• Authors: Jie-En Yao, Li-Yuan Tsao, Yi-Chen Lo, Roy Tseng, Chia-Che Chang, Chun-Yi Lee
• Abstract summary: We propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. We show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.
• Score: 11.653044501483667
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Flow-based methods have demonstrated promising results in addressing the ill-posed nature of super-resolution (SR) by learning the distribution of high-resolution (HR) images with the normalizing flow. However, these methods can only perform a predefined fixed-scale SR, limiting their potential in real-world applications. Meanwhile, arbitrary-scale SR has gained more attention and achieved great progress. Nonetheless, previous arbitrary-scale SR methods ignore the ill-posed problem and train the model with per-pixel L1 loss, leading to blurry SR outputs. In this work, we propose "Local Implicit Normalizing Flow" (LINF) as a unified solution to the above problems. LINF models the distribution of texture details under different scaling factors with normalizing flow. Thus, LINF can generate photo-realistic HR images with rich texture details in arbitrary scale factors. We evaluate LINF with extensive experiments and show that LINF achieves the state-of-the-art perceptual quality compared with prior arbitrary-scale SR methods.

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