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Remote Sensing Image Denoising Algorithm Based on Improved Transformer Network

2024 International Conference on Optoelectronic Information and Optical Engineering (OIOE 2024)(2024)

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Abstract
In the current optical remote sensing field, it has continuously evolved into a multi-layered and multi-perspective observation system. Faced with the complexities of observation tasks, diverse observation methods, and the refinement of observation targets, there is a need for more in-depth research on denoising of remote sensing images. Traditional denoising algorithms often produce denoised images with overly smooth edge textures, leading to the loss of small targets within the images. Therefore, addressing the aforementioned issues, this paper proposes an improved denoising algorithm based on the Transformer network structure. This algorithm employs attention operations across channel dimensions and utilizes feature recalibration. This allows the model to determine the importance of various feature channels, thereby avoiding the significant computational overhead brought about by the traditional Transformer's self-attention enhancement in spatial dimensions. Moreover, the algorithm utilizes a U-shaped denoising module, which effectively reduces the semantic gap between image feature mappings, resulting in the restoration of better image features. The experiments indicate that when tested on remote sensing image datasets, the proposed algorithm outperforms current representative algorithms in both subjective and objective evaluation metrics. While effectively removing image noise, it also better preserves edge details and texture features, achieving superior visual results.
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要点】:本文提出了一种基于改进Transformer网络的遥感图像去噪算法,通过跨通道注意力操作和特征重校准,有效提升边缘细节和纹理特征的保留,优于现有代表性算法。

方法】:算法采用改进的Transformer网络结构,通过在通道维度上执行注意力操作,并利用特征重校准技术,以确定不同特征通道的重要性。

实验】:实验在遥感图像数据集上进行,结果表明该算法在主观和客观评价指标上均优于当前代表性算法,有效去噪同时更好地保留边缘细节和纹理特征。