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

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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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Benchmark
Image Super-Resolution DIV2K val - 4x upscaling LINF PSNR 27.33 # 10
SSIM 0.76 # 12
LPIPS 0.112 # 3
Image Super-Resolution DIV2K val - 4x upscaling LINF t=0.0 PSNR 29.14 # 4
SSIM 0.83 # 5
LPIPS 0.248 # 6

Methods


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