Patch-Divided Flexible and Diverse Super-Resolution Style Transfer
Guoren Yao, Gaoming Yang, Xintian Liu · Electronics · 2026
Recently, super-resolution transmission methods have been introduced on limited GPU. However, due to the stereotyped style exchange process, the GPU consumption and diversity of super-resolution style transfer are still challenging. In addition, existing methods can inadvertently lead to content leakage and uneven stroke size distribution, resulting in less attractive results. Hence, we introduce a fast and diverse super-resolution transfer (FDST) model, which can realize more flexible super-resolution multi-style transfer by mapping noise and information from another style in the style encoder. In addition, we propose two loss functions within the existing framework to support the preservation of content structure: Patch Content-Consistent Patch Loss (Patch-CCPL) and Patch contrastive loss. The proposed method effectively and elaborately integrates colors and texture structures. The key idea of FDST is to divide the super-resolution image into small patches, and then perform diverse style conversions on each small patch by injecting subtle noise or sub-style images. We implemented theoretical analyses and extensive results to qualitatively and quantitatively evaluate our method and compare it with the state-of-the-art algorithm. Extensive experiments on 4K content images demonstrate that FDST achieves a user preference score of 0.207, SSIM of 0.492, and LPIPS of 0.526, outperforming existing methods in content preservation while requiring only 2.573 GB model storage. Ablation studies confirm the contribution of each component, and a discussion of security applications including watermarking and forensic analysis is provided.