Arbitrary-Resolution and Arbitrary-Scale Face Super-Resolution with Implicit Representation Networks

• URL: http://arxiv.org/abs/2511.16341v1
• Date: Thu, 20 Nov 2025 13:21:58 GMT
• Title: Arbitrary-Resolution and Arbitrary-Scale Face Super-Resolution with Implicit Representation Networks
• Authors: Yi Ting Tsai, Yu Wei Chen, Hong-Han Shuai, Ching-Chun Huang,
• Abstract summary: Face super-resolution (FSR) is a critical technique for enhancing low-resolution facial images.<n>This paper introduces an Arbitrary-Resolution and Arbitrary-Scale FSR method with implicit representation networks (ARASFSR)<n>ARASFSR employs 2D deep features, local relative coordinates, and up-sampling scale ratios to predict RGB values for each target pixel, allowing super-resolution at any up-sampling scale.
• Score: 37.075582998671905
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Face super-resolution (FSR) is a critical technique for enhancing low-resolution facial images and has significant implications for face-related tasks. However, existing FSR methods are limited by fixed up-sampling scales and sensitivity to input size variations. To address these limitations, this paper introduces an Arbitrary-Resolution and Arbitrary-Scale FSR method with implicit representation networks (ARASFSR), featuring three novel designs. First, ARASFSR employs 2D deep features, local relative coordinates, and up-sampling scale ratios to predict RGB values for each target pixel, allowing super-resolution at any up-sampling scale. Second, a local frequency estimation module captures high-frequency facial texture information to reduce the spectral bias effect. Lastly, a global coordinate modulation module guides FSR to leverage prior facial structure knowledge and achieve resolution adaptation effectively. Quantitative and qualitative evaluations demonstrate the robustness of ARASFSR over existing state-of-the-art methods while super-resolving facial images across various input sizes and up-sampling scales.

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