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- W4312356069 abstract "To eliminate stripe noise from the single infaring image of a meteorological satellite, a new deep network design is being proposed. Existing methods of fixed modes for noise reduction can easily be exaggerated by the movement status of the sight and operating conditions of the image sensors, leading to smoothing belongings, ghosted, and slow conversion. We create an advanced neural network model with residual skipping connections in case of cascade-free single frame blind operation to resolve these issues. Furthermore, a common very well convolution unit is introduced to extract and fuse additional features over many scales to extract additional spatial information. In infrared imaging systems, stripe noise greatly degrades image quality. Continue to strike a balance between noise suppression, protection of information, and real-time precision, restricting their use in spectral imaging and signal processing, with existing striping algorithms. The suggested network can be trained and tested using a new set of infrared cloud photos from meteorological satellites. After these tests, it was discovered that the proposed technique recovered photographs with a similar level of quality and efficiency to other cutting-edge technologies. This paper presents a revolutionary deep-neural network of wavelets from a transformative domain. To tackle the problem perspectives which utilizes the intrinsic properties of stripe noise and additional information between different wavelets. The sub-bands coefficients are used to accurately estimate the noise at a low computational charge." @default.
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- W4312356069 date "2022-01-01" @default.
- W4312356069 modified "2023-09-29" @default.
- W4312356069 title "Using Convolution Networks to Remove Stripes Noise from Infrared Cloud Images" @default.
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- W4312356069 doi "https://doi.org/10.1007/978-3-031-21385-4_43" @default.
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