Ms. Sunwoo Cho Profile

Sunwoo Cho

at Seoul National Univ

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Proceedings Article | 1 May 2022 Paper

Kernel estimation for super-resolution with flow-based kernel prior

Sunwoo Cho, Nam Ik Cho

Proceedings Volume 12177, 1217739 (2022) https://doi.org/10.1117/12.2625958

KEYWORDS: Super resolution, Lawrencium, Image analysis, Gallium nitride, Image quality, Image processing, Convolution, Process modeling, Performance modeling, Error analysis

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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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