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The speckle noise found in synthetic aperture radar (SAR) images severely affects the efficiency of image interpretation, retrieval and other applications. Thus, effective methods for despeckling SAR image are required. The traditional methods for SAR image despeckling fail to balance in terms of the relationship between the intensity of speckle noise filtering and the retention of texture details. Deep learning based SAR image despeckling methods have been shown to have the potential to achieve this balance. Therefore, this study proposes a self-attention multi-scale convolution neural network (SAMSCNN) method for SAR image despeckling. The advantage of the SAMSCNN method is that it considers both multi-scale feature extraction and channel attention mechanisms for multi-scale fused features. In the SAMSCNN method, multi-scale features are extracted from SAR images through convolution layers with different depths. These are concatenated; then, and an attention mechanism is introduced to assign different weights to features of different scales, obtaining multi-scale fused features with weights. Finally, the despeckled SAR image is generated through global residual noise reduction and image structure fine-tuning. The despeckling experiments in this study involved a variety of scenes using simulated and real data. The performance of the proposed model was analysed using quantitative and qualitative evaluation methods and compared to probabilistic patch-based (PPB), SAR block-matching 3-D (SAR-BM3D) and SAR-CNN methods. The experimental results show that the method proposed in this paper improves the objective indexes and shows great advantages in visual effects compared to these classical methods. The method proposed in this study can provide key technical support for the practical application of SAR images. [ABSTRACT FROM AUTHOR]

Acoustic tomography is a widely used non-contact method for visualizing temperature distribution. A temperature distribution reconstruction algorithm based on an improved sparse reconstruction model and multi-scale feature fusion network is proposed. First, the acoustic temperature measurement sparse reconstruction model is improved by combining the error function (ERF) and the iterative reweighting algorithm, and the alternating direction method of multipliers algorithm (ADMM) is used to solve the model to obtain the initial temperature distribution. Then the feature extraction network is constructed to extract multi-scale features of acoustic time of flight (TOF) as prior information. Finally, the feature fusion reconstruction network is constructed to fuse and reconstruct the initial temperature distribution and multi-scale features to obtain a high-precision temperature distribution. Simulation and experimental tests were conducted respectively, and compared with other algorithms. The results show that the average relative error and root mean square error of the simulated temperature distribution reconstruction are 0.073% and 0.1% respectively, the average reconstruction error of the temperature points set in the experimental test is 0.38%, and the reconstruction errors are lower than other algorithms. The proposed method effectively utilizes prior information to correct sparse reconstruction temperature distribution reconstruction results, significantly improving the quality of temperature distribution reconstruction. [Display omitted] • Acoustic tomography technology is used for temperature distribution reconstruction. • The improved sparse reconstruction model can improve the accuracy of initial temperature distribution reconstruction. • The feature extraction network can perform multi-scale feature extraction on the time-of-flight of acoustic waves. • The feature fusion reconstruction network can improve the utilization of prior information. [ABSTRACT FROM AUTHOR]

The spatial resolution of precipitation predicted by general circulation models is too coarse to meet current research and operational needs. Downscaling is one way to provide finer resolution data at local scales. The single-image super-resolution method in the computer vision field has made great strides lately and has been applied in various fields. In this article, we propose a novel reference-based and gradient-guided deep learning model (RBGGM) to downscale daily precipitation considering the discontinuity of precipitation and ill-posed nature of downscaling. Global Precipitation Measurement Mission (GPM) precipitation data, variables in ERA5 re-analysis data, and topographic data are selected to perform the downscaling, and a residual dense attention block is constructed to extract features of them. By exploring the discontinuous feature of precipitation, we introduce gradient feature to reconstruct precipitation distribution. We also extract the feature of high-resolution monthly precipitation as a reference feature to resolve the ill-posed nature of downscaling. Extensive experimental results on benchmark data sets demonstrate that our proposed model performs better than other baseline methods. Furthermore, we construct a daily precipitation downscaling data set based on GPM precipitation data, ERA5 re-analysis data and topographic data. [ABSTRACT FROM AUTHOR]