SCEESR: Semantic-Control Edge Enhancement for Diffusion-Based Super-Resolution

• URL: http://arxiv.org/abs/2510.19272v1
• Date: Wed, 22 Oct 2025 06:06:01 GMT
• Title: SCEESR: Semantic-Control Edge Enhancement for Diffusion-Based Super-Resolution
• Authors: Yun Kai Zhuang,
• Abstract summary: Real-world image super-resolution must handle complex degradations and inherent reconstruction ambiguities.<n>One-step diffusion models offer speed but often produce structural inaccuracies due to distillation artifacts.<n>We propose a novel SR framework that enhances a one-step diffusion model using a ControlNet mechanism for semantic edge guidance.
• Score: 0.8122270502556375
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world image super-resolution (Real-ISR) must handle complex degradations and inherent reconstruction ambiguities. While generative models have improved perceptual quality, a key trade-off remains with computational cost. One-step diffusion models offer speed but often produce structural inaccuracies due to distillation artifacts. To address this, we propose a novel SR framework that enhances a one-step diffusion model using a ControlNet mechanism for semantic edge guidance. This integrates edge information to provide dynamic structural control during single-pass inference. We also introduce a hybrid loss combining L2, LPIPS, and an edge-aware AME loss to optimize for pixel accuracy, perceptual quality, and geometric precision. Experiments show our method effectively improves structural integrity and realism while maintaining the efficiency of one-step generation, achieving a superior balance between output quality and inference speed. The results of test datasets will be published at https://drive.google.com/drive/folders/1amddXQ5orIyjbxHgGpzqFHZ6KTolinJF?usp=drive_link and the related code will be published at https://github.com/ARBEZ-ZEBRA/SCEESR.

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