HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior

• URL: http://arxiv.org/abs/2411.18662v1
• Date: Wed, 27 Nov 2024 15:22:44 GMT
• Title: HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
• Authors: Li-Yuan Tsao, Hao-Wei Chen, Hao-Wei Chung, Deqing Sun, Chun-Yi Lee, Kelvin C. K. Chan, Ming-Hsuan Yang,
• Abstract summary: We present HoliSDiP, a framework that leverages semantic segmentation to provide both precise textual and spatial guidance for Real-ISR. Our method employs semantic labels as concise text prompts while introducing dense semantic guidance through segmentation masks and our proposed spatial-CLIP Map.
• Score: 62.04939047885834
• License:
• Abstract: Text-to-image diffusion models have emerged as powerful priors for real-world image super-resolution (Real-ISR). However, existing methods may produce unintended results due to noisy text prompts and their lack of spatial information. In this paper, we present HoliSDiP, a framework that leverages semantic segmentation to provide both precise textual and spatial guidance for diffusion-based Real-ISR. Our method employs semantic labels as concise text prompts while introducing dense semantic guidance through segmentation masks and our proposed Segmentation-CLIP Map. Extensive experiments demonstrate that HoliSDiP achieves significant improvement in image quality across various Real-ISR scenarios through reduced prompt noise and enhanced spatial control.

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