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Kernel Estimation for super-resolution with Flow-based Kernel Prior
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- Authors
- Issue Date
- 2022-01
- Publisher
- SPIE-INT SOC OPTICAL ENGINEERING
- Citation
- INTERNATIONAL WORKSHOP ON ADVANCED IMAGING TECHNOLOGY (IWAIT) 2022, Vol.12177, p. 1217739
- Abstract
- Single-Image Super-Resolution methods typically assume that a low-resolution image is degraded from a high-resolution one through "bicubic" kernel convolution followed by downscaling. However, this induces a domain gap between training image datasets and the real scenario's test images, which are down-sampled from the images that underwent convolution with arbitrary unknown kernels. Hence, correct kernel estimation for a given real-world image is necessary for its better super-resolution. One of the kernel estimation methods, KernelGAN(1) locates the input image in the same domain of high-resolution image for accurate estimation. However, using only a low-resolution image cannot fully utilize the high-frequency information in the original image. To increase the estimation accuracy, we adopt a superresolved image for kernel estimation. Also, we use a flow-based kernel prior to getting a reasonable super-resolved image and stabilize the whole estimation process.
- ISSN
- 0277-786X
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