DHTCUN: Deep Hybrid Transformer CNN U Network for Single-Image Super-Resolution

Abstract

Recent advances in image super-resolution have investigated various transformer and CNN techniques to improve quantitative and perceptual outcomes. Reconstructing high-resolution images from their low-resolution equivalents by combining the power of transformers and CNN has been a crucial task in recent times. We propose a novel U-shaped architecture that integrates transformers and convolutional neural networks (CNNs) to leverage the strengths of both approaches. The network incorporates a novel Parallel Hybrid Transformer CNN Block (PHTCB) on the backbone of the U-shaped design, ensuring computational efficiency and robust hierarchical feature representation. Our architecture incorporates triple-enhanced spatial-attention mechanisms and a Transformer CNN (TCN) Block in PHTCB. The TCN Block helps preserve sharp edges and intricate details often lost in traditional SISR methods and enhances the visual fidelity of the reconstructed high-resolution images. Additionally, we introduce the triple-enhanced spatial attention (TESA) approach that helps precisely localize of important features. Blurring can be reduced for crucial features by focusing on these critical areas because of the network’s ability to control features at various scales. Experiments demonstrate that our proposed method yields better quantitative measurements, including visually appealing high-resolution image reconstructions, peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM).

Keywords

• CNN
• enhanced spatial attention
• single-image super-resolution
• Transformer