DSCLoRA applies low-rank adaptation techniques from large language models to image super-resolution, boosting performance without increasing computational complexity.

• Introduces convolutional low-rank adaptation specifically designed for CNN-based image processing
• Employs knowledge distillation to effectively transfer learning from larger models
• Achieves superior image quality while maintaining lightweight model architecture
• Demonstrates 5.43% PSNR improvement with minimal parameter increase (only 5.77%)

For engineering teams, this research offers a practical approach to enhance image processing applications without the computational costs typically associated with performance improvements.

Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution