基于轻量级和笔触的超高清图像艺术风格迁移OA

Objective With the continuous advancement of modern technology,image resolution and user demands continue to increase.However,limited GPU memory frequently causes out-of-memory errors during ultra-high-resolution(UHR)style transfer tasks.Furthermore,existing UHR artistic transfer methods often neglect brushstroke characteristics,predominantly relying on small brushstrokes for style transfer.To address these challenges,this paper presents a lightweight framework integrating patch cutting,termed the Patch Cutting and Lightweight Model(PCLM).Methods To effectively reduce memory consumption,the proposed model employed a dual optimization strategy.First,UHR images were segmented into patch collections,a process that broke down a large-scale task into smaller subtasks to fundamentally address the memory bottleneck.Simultaneously,knowledge distillation was utilized to compress model parameters.In addition,a patch relation loss(PRL)was proposed to control both brushstroke size and patch consistency during style transfer.Results Extensive qualitative and quantitative experiments demonstrated that PCLM successfully realized UHR style transfer with large brushstrokes under constrained memory conditions.While maintaining a minimal memory footprint,the model achieved the following evaluation metrics on the test set:0.495 928(SSIM),11.610 026 dB(PSNR),0.546 720(LPIPS),and 3.373 454 GB(GPU memory usage).Conclusion Theoretical analysis and empirical results confirm that PCLM effectively resolves the memory consumption challenge and generates high-quality UHR artistic paintings featuring large brushstrokes.This study provides a valuable reference for subsequent UHR image processing tasks.