Transformer for Single Image Super-Resolution (2108.11084v3)

Abstract: Single image super-resolution (SISR) has witnessed great strides with the development of deep learning. 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. In this paper, we propose a novel Efficient Super-Resolution Transformer (ESRT) for SISR. ESRT is a hybrid model, which consists of a Lightweight CNN Backbone (LCB) and a Lightweight Transformer Backbone (LTB). Among them, LCB can dynamically adjust the size of the feature map to extract deep features with a low computational cost. LTB is composed of a series of Efficient Transformers (ET), which occupies a small GPU memory occupation, thanks to the specially designed Efficient Multi-Head Attention (EMHA). Extensive experiments show that ESRT achieves competitive results with low computational costs. Compared with the original Transformer which occupies 16,057M GPU memory, ESRT only occupies 4,191M GPU memory. All codes are available at https://github.com/luissen/ESRT.

Overview of "Transformer for Single Image Super-Resolution"

The paper "Transformer for Single Image Super-Resolution" presents a novel approach to enhancing the efficacy of Single Image Super-Resolution (SISR) tasks by integrating Transformer models, traditionally used in NLP, with Convolutional Neural Networks (CNNs). This integration seeks to address the challenges posed by high computational and memory costs associated with vision transformers, mitigating these issues through the Efficient Super-Resolution Transformer (ESRT) model. ESRT combines a Lightweight CNN Backbone (LCB) with a Lightweight Transformer Backbone (LTB), thus leveraging the strengths of both architectures.

Methodology and Architecture

The ESRT model is constructed with a focus on reducing the computational burden while maintaining high-performance levels in SISR. The architecture is strategically separated into two primary components:

1. Lightweight CNN Backbone (LCB): This module uses High Preserving Blocks (HPBs) that integrate High-frequency Filtering Modules (HFMs) and Adaptive Residual Feature Blocks (ARFBs) to dynamically adjust the size of feature maps. This design reduces computational expense while preserving vital high-frequency information necessary for accurate image restoration.
2. Lightweight Transformer Backbone (LTB): LTB employs an Efficient Transformer (ET), which includes the Efficient Multi-Head Attention (EMHA) mechanism. EMHA reduces memory consumption by focusing on relationships between local image blocks, contrary to standard methods that might involve broader, more computationally expensive operations.

The integration of these components allows ESRT to operate with significantly lower GPU memory usage compared to traditional Transformer models—occupying only 4,191M memory versus the original Transformer's 16,057M—without sacrificing performance.

Experimental Results

The efficacy of the ESRT model has been demonstrated through extensive experimentation on multiple benchmark datasets, including Set5, Set14, BSD100, Urban100, and Manga109. The results underscore ESRT's ability to achieve competitive performance with reduced computational demands. Specifically, ESRT outperforms other lightweight models in various metrics across different image scales. For example, ESRT provides superior results on Urban100, highlighting its capacity to utilize similar patches within an image to enhance super-resolution tasks.

Implications and Future Directions

The introduction of ESRT in SISR showcases the potential benefits of hybrid learning architectures that combine CNNs and Transformers. This model not only alleviates the high memory and computation requirements associated with transformers in vision tasks but also lays groundwork for further investigation into efficient learning architectures. Future directions could involve extending ESRT's principles to other image processing domains, exploring further optimizations of the hybrid structure, and potentially integrating such models into resource-constrained devices where model efficiency is critical.

In conclusion, the "Transformer for Single Image Super-Resolution" paper contributes meaningful advancements to the domain of image super-resolution through its innovative use of transform-based architectures in conjunction with traditional CNN elements. This research underscores significant steps towards bridging the gap between high-performance model outcomes and real-world applicability.