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30 April 2022 Kernel estimation for super-resolution with flow-based kernel prior
Sunwoo Cho, Nam Ik Cho
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Proceedings Volume 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022; 1217739 (2022) https://doi.org/10.1117/12.2625958
Event: International Workshop on Advanced Imaging Technology 2022 (IWAIT 2022), 2022, Hong Kong, China
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 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.
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Sunwoo Cho and Nam Ik Cho "Kernel estimation for super-resolution with flow-based kernel prior", Proc. SPIE 12177, International Workshop on Advanced Imaging Technology (IWAIT) 2022, 1217739 (30 April 2022); https://doi.org/10.1117/12.2625958
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KEYWORDS
Super resolution
Lawrencium
Image analysis
Gallium nitride
Image processing
Image quality
Convolution
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