Exploring Sparsity in Image Super-Resolution for Efficient Inference (2006.09603v2)

Abstract: Current CNN-based super-resolution (SR) methods process all locations equally with computational resources being uniformly assigned in space. However, since missing details in low-resolution (LR) images mainly exist in regions of edges and textures, less computational resources are required for those flat regions. Therefore, existing CNN-based methods involve redundant computation in flat regions, which increases their computational cost and limits their applications on mobile devices. In this paper, we explore the sparsity in image SR to improve inference efficiency of SR networks. Specifically, we develop a Sparse Mask SR (SMSR) network to learn sparse masks to prune redundant computation. Within our SMSR, spatial masks learn to identify "important" regions while channel masks learn to mark redundant channels in those "unimportant" regions. Consequently, redundant computation can be accurately localized and skipped while maintaining comparable performance. It is demonstrated that our SMSR achieves state-of-the-art performance with 41%/33%/27% FLOPs being reduced for x2/3/4 SR. Code is available at: https://github.com/LongguangWang/SMSR.

• The paper introduces a sparse mask learning technique that uses spatial and channel masks to focus computation on image details.
• It achieves FLOP reductions of 41%, 33%, and 27% for 2x, 3x, and 4x upscaling, enhancing efficiency on resource-limited devices.
• The study leverages differentiable training via the Gumbel softmax trick to optimize both the masks and CNN weights concurrently.

Analysis of Sparsity in CNN-Based Image Super-Resolution for Enhanced Efficiency

The paper "Exploring Sparsity in Image Super-Resolution for Efficient Inference" addresses the computational inefficiencies inherent in conventional Convolutional Neural Network (CNN)-based Image Super-Resolution (SR) methods. These traditional methods typically allocate computational resources uniformly across an input image, which leads to superfluous computation, particularly in flat regions that do not require extensive processing compared to regions with edges and textures. This inefficient resource allocation constrains the applicability of such models in resource-limited environments, such as smartphones and other edge devices.

Core Contributions

The authors propose a novel Sparse Mask Super-Resolution (SMSR) network, which utilizes sparsity to optimize the computational resource allocation in SR tasks. Significant contributions of the SMSR network include:

1. Sparse Mask Learning: The introduction of dual masks—spatial masks to distinguish important'' (edge and texture) regions, and channel masks to identify and prune redundant channels withinunimportant'' regions (flat areas). This design reduces unnecessary calculations while ensuring critical details receive sufficient computation.
2. Efficiency with Performance Integrity: The SMSR network achieved notable efficiency, reducing floating-point operations (FLOPs) by 41%/33%/27% for $\times2/3/4$ SR models respectively, while maintaining state-of-the-art super-resolution image quality across several benchmark datasets. Such efficiency improvements afford substantial FLOPs reductions crucial for performance on mobile devices.
3. Differentiable Mask Training: Through the Gumbel softmax trick, the authors ensured that sparse mask learning is differentiable during training. This allows the training process to concurrently optimize both the masks and the convolutional weights.
4. Broad Baseline Evaluation: Experimental results positioned the SMSR network not only as an efficient solution but also one that outperforms state-of-the-art SR methods in terms of both PSNR/SSIM quality metrics and reduced processing demands.

Implications and Future Directions

The SMSR network's approach to leveraging sparsity represents a pivotal tactic in the advancement towards computationally-efficient super-resolution, enabling its deployment on low-power edge devices. This work is significant in light of the growing demand for high-resolution visual data processing in mobile and embedded systems.

The approach raises pertinent questions and opportunities for further research:

• Extension to Other Vision Tasks: The sparsity technique could potentially be adapted for other computationally demanding applications in computer vision where certain spatial or channel data is inherently less informative.
• Hardware Optimization: Exploring targeted hardware accelerations, such as sparse convolution optimizations, could further mitigate latency introduced by sparse algorithms on general-purpose hardware like GPUs.
• Dynamic Sparsity and Sensitivity: Investigating how adaptive sparsity could be employed, where mask predictions evolve with respect to the input image characteristics, may further enhance performance efficiency.

This paper is instrumental in highlighting the potential of dynamic sparsity-based approaches to solve real-world constraints in the field of image processing on limited-resource devices, setting a promising precedent for the efficient deployment of CNN-based models.