MambaLiteSR: Image Super-Resolution with Low-Rank Mamba using Knowledge Distillation
Romina Aalishah, Mozhgan Navardi, Tinoosh Mohsenin

TL;DR
MambaLiteSR is a lightweight, efficient image super-resolution model optimized for edge devices, utilizing knowledge distillation and low-rank approximation to reduce power consumption and parameters while maintaining high image quality.
Contribution
The paper introduces MambaLiteSR, a novel lightweight SR model that combines Vision Mamba architecture with knowledge distillation and low-rank approximation for resource-efficient deployment.
Findings
Outperforms state-of-the-art edge SR methods in power efficiency
Uses 15% fewer parameters with minimal performance loss
Achieves up to 58% power reduction on embedded devices
Abstract
Generative Artificial Intelligence (AI) has gained significant attention in recent years, revolutionizing various applications across industries. Among these, advanced vision models for image super-resolution are in high demand, particularly for deployment on edge devices where real-time processing is crucial. However, deploying such models on edge devices is challenging due to limited computing power and memory. In this paper, we present MambaLiteSR, a novel lightweight image Super-Resolution (SR) model that utilizes the architecture of Vision Mamba. It integrates State Space Blocks and a reconstruction module for efficient feature extraction. To optimize efficiency without affecting performance, MambaLiteSR employs knowledge distillation to transfer key insights from a larger Mamba-based teacher model to a smaller student model via hyperparameter tuning. Through mathematical analysis…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Image and Signal Denoising Methods · AI in cancer detection
MethodsSoftmax · Attention Is All You Need · Knowledge Distillation · Mamba: Linear-Time Sequence Modeling with Selective State Spaces
