A Low-Resolution Image is Worth 1x1 Words: Enabling Fine Image Super-Resolution with Transformers and TaylorShift

The TaylorIR framework introduces a novel approach to image super-resolution by employing 1x1 patch embeddings and substituting traditional self-attention mechanisms with TaylorShift. This design enhances pixel-level fidelity, allowing for more precise image reconstruction. Additionally, TaylorIR improves the scalability of transformer-based models, addressing a common limitation in existing architectures. These advancements collectively contribute to significant improvements in the quality of image reconstruction. By refining both the embedding strategy and attention mechanism, TaylorIR offers a promising direction for future developments in image super-resolution. The framework's potential impact lies in its ability to produce higher-quality images from low-resolution inputs, which could benefit various applications in computer vision and related fields.