{"ID":2848849,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.23978","arxiv_id":"2510.23978","title":"Efficient Cost-and-Quality Controllable Arbitrary-scale Super-resolution with Fourier Constraints","abstract":"Cost-and-Quality (CQ) controllability in arbitrary-scale super-resolution is crucial. Existing methods predict Fourier components one by one using a recurrent neural network. However, this approach leads to performance degradation and inefficiency due to independent prediction. This paper proposes predicting multiple components jointly to improve both quality and efficiency.","short_abstract":"Cost-and-Quality (CQ) controllability in arbitrary-scale super-resolution is crucial. Existing methods predict Fourier components one by one using a recurrent neural network. However, this approach leads to performance degradation and inefficiency due to independent prediction. This paper proposes predicting multiple c...","url_abs":"https://arxiv.org/abs/2510.23978","url_pdf":"https://arxiv.org/pdf/2510.23978v1","authors":"[\"Kazutoshi Akita\",\"Norimichi Ukita\"]","published":"2025-10-28T01:19:54Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}