Spectral super-resolution, which reconstructs hyperspectral images (HSI) from a single RGB image, has garnered increasing attention. Due to the limitations of CNN structures in spectral modeling and the high computati...
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Low-light image enhancement aims to improve visual quality under challenging lighting conditions while preserving details and color fidelity. Existing traditional algorithms and deep learning approaches, often struggl...
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The scarcity of labeled data poses a significant challenge for deep learning-based medical image segmentation. To address this, this study introduces the novel Foundation Model-based Few-Shot Segmentation (FM-FSS) par...
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State space models (SSMs), particularly Mamba, have shown promise in NLP tasks and are increasingly applied to vision tasks. However, most Mamba-based vision models focus on network architecture and scan paths, with l...
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image sensors are widely employed across various industries, including smart mobile devices, autonomous vehicles, and surveillance systems. The image Sensor Interface processing Unit is typically characterized by a hi...
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Pan-sharpening is a commonly employed strategy to obtain high-resolution multispectral (HRMS) images. Existing deep unfolding networks for pan-sharpening suffer from ineffectively establishing the relationship between...
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The rapid growth in digital image sharing, driven by advancements in internet and communication technologies, has raised concerns about image integrity, especially in sensitive fields like healthcare. This paper prese...
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Reconstruction of hyperspectral images (HSIs) from their RGB measurements is an ill-posed inverse problem. The key to successful reconstruction relies on establishing an effective HSI prior, for which deep learning te...
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Existing deep learning-based models can achieve a prompt diagnosis of operational anomalies by analyzing the audios emitted from power transformers. However, the practical abnormal data are insufficient for model trai...
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Recent advancements in deep neural networks have shown remarkable improvements in image quality during the demosaicking process, surpassing conventional algorithms. However, these deep neural network techniques are of...
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