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.
KEYWORDS: Remote sensing, Cameras, Interference (communication), Data modeling, Denoising, Dark current, Signal to noise ratio, Process modeling, Convolution, Image processing
With the continuous development of the field of remote sensing, the field of space-based remote sensing is developing in the direction of all-sky time and intelligence. Since low-light remote sensing is used to detect ground objects under low illumination conditions such as night and dawn and dusk, it leads to the characteristics of low contrast, low brightness and low signal-to-noise ratio of remote sensing images. The low signal-to-noise ratio leads to a large number of complex physical noises which will drown the image features and seriously affect the recognition and interpretation of ground objects. In this paper, a real physical model construction method based on physical mechanism of low-light image is proposed, By solving the noise parameters of space-based low-light remote sensing camera, the specific noise distribution model is constructed, which is used to synthesize the training data of the training denoising network, Thus, it gets rid of the difficult and laborious calibration problem caused by the lack of space-based data. In addition, in order to simulate the real imaging scene of space-based remote sensing camera, a set of low-light remote sensing noise image data set based on real physical model is constructed for the first time, which effectively promotes the development of subsequent image denoising research.
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