Transformer for Single Image Super-Resolution

🤖 Plain-English Summary

Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. Compared with the original Transformer which occupies 16,057M GPU memory, ESRT only occupies 4,191M GPU memory.

🔑 Key Findings

• However, most existing studies focus on building more complex networks with a massive number of layers.
• Recently, more and more researchers start to explore the application of Transformer in computer vision tasks.
• However, the heavy computational cost and high GPU memory occupation of the vision Transformer cannot be ignored.

🔗 Related Resources