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New T-PMambaSR framework enhances image super-resolution with Mamba and attention

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced T-PMambaSR, a new lightweight framework for image super-resolution that combines window-based self-attention with Progressive Mamba. This approach aims to capture global receptive fields efficiently, overcoming the quadratic complexity of Transformer-based methods. The framework also includes an Adaptive High-Frequency Refinement Module to restore lost high-frequency details, demonstrating competitive performance with lower computational costs. AI

IMPACT This research offers a more computationally efficient method for image super-resolution, potentially improving performance in applications requiring high-fidelity image processing.

RANK_REASON This is a research paper detailing a new technical approach to image super-resolution. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New T-PMambaSR framework enhances image super-resolution with Mamba and attention

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This is a research paper detailing a new technical approach to image super-resolution. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sichen Guo, Wenjie Li, Yuanyang Liu, Guangwei Gao, Jian Yang, Chia-Wen Lin ·

    Transformer-Progressive Mamba Network for Lightweight Image Super-Resolution

    arXiv:2511.03232v3 Announce Type: replace Abstract: Recently, Mamba-based super-resolution (SR) methods have demonstrated the ability to capture global receptive fields with linear complexity, addressing the quadratic computational cost of Transformer-based SR approaches. However…

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