Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models

• URL: http://arxiv.org/abs/2403.17902v2
• Date: Wed, 29 May 2024 20:43:07 GMT
• Title: Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models
• Authors: Mohammad Shahab Sepehri, Zalan Fabian, Mahdi Soltanolkotabi,
• Abstract summary: Serpent is an efficient architecture for high-resolution image restoration. We show that Serpent can achieve reconstruction quality on par with state-of-the-art techniques.
• Score: 22.702352459581434
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The landscape of computational building blocks of efficient image restoration architectures is dominated by a combination of convolutional processing and various attention mechanisms. However, convolutional filters, while efficient, are inherently local and therefore struggle with modeling long-range dependencies in images. In contrast, attention excels at capturing global interactions between arbitrary image regions, but suffers from a quadratic cost in image dimension. In this work, we propose Serpent, an efficient architecture for high-resolution image restoration that combines recent advances in state space models (SSMs) with multi-scale signal processing in its core computational block. SSMs, originally introduced for sequence modeling, can maintain a global receptive field with a favorable linear scaling in input size. We propose a novel hierarchical architecture inspired by traditional signal processing principles, that converts the input image into a collection of sequences and processes them in a multi-scale fashion. Our experimental results demonstrate that Serpent can achieve reconstruction quality on par with state-of-the-art techniques, while requiring orders of magnitude less compute (up to $150$ fold reduction in FLOPS) and a factor of up to $5\times$ less GPU memory while maintaining a compact model size. The efficiency gains achieved by Serpent are especially notable at high image resolutions.

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