CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution

TL;DR

CubeFormer introduces a novel 3D attention mechanism and sampling strategies to significantly improve lightweight image super-resolution, achieving state-of-the-art results with enhanced feature diversity and detail recovery.

Contribution

The paper proposes CubeFormer, a simple baseline that enhances feature diversity using cube attention and transformer blocks for improved lightweight super-resolution.

Findings

Achieves state-of-the-art performance on SR benchmarks.

Enhances feature diversity and detail recovery.

Introduces cube attention and intra/inter-cube transformer blocks.

Abstract

Lightweight image super-resolution (SR) methods aim at increasing the resolution and restoring the details of an image using a lightweight neural network. However, current lightweight SR methods still suffer from inferior performance and unpleasant details. Our analysis reveals that these methods are hindered by constrained feature diversity, which adversely impacts feature representation and detail recovery. To respond this issue, we propose a simple yet effective baseline called CubeFormer, designed to enhance feature richness by completing holistic information aggregation. To be specific, we introduce cube attention, which expands 2D attention to 3D space, facilitating exhaustive information interactions, further encouraging comprehensive information extraction and promoting feature variety. In addition, we inject block and grid sampling strategies to construct intra-cube transformer blocks (Intra-CTB) and inter-cube transformer blocks (Inter-CTB), which perform local and global modeling, respectively. Extensive experiments show that our CubeFormer achieves state-of-the-art performance on commonly used SR benchmarks. Our source code and models will be publicly available.