Transformer models have great potential in the field of remote sensing super-resolution (SR) due to their excellent self-attention mechanisms. However, transformer models are prone to overfitting because of their large number of parameters, especially with the typically small remote sensing datasets. Additionally, the reliance of transformer-based SR models on convolution-based upsampling often leads to mismatched semantic information. To tackle these challenges, we propose an efficient super-resolution hybrid network (EHNet) based on the encoder composed of our designed lightweight convolution module and the decoder composed of an improved swin transformer. The encoder, featuring our novel Lightweight Feature Extraction Block (LFEB), employs a more efficient convolution method than depthwise separable convolution based on depthwise convolution. Our LFEB also integrates a Cross Stage Partial structure for enhanced feature extraction. In terms of the decoder, based on the swin transformer, we innovatively propose a sequence-based upsample block (SUB) for the first time, which directly uses the sequence of tokens in the transformer to focus on semantic information through the MLP layer, which enhances the feature expression ability of the model and improves the reconstruction accuracy. Experiments show that EHNet’s PSNR on UCMerced and AID datasets obtains a SOTA performance of 28.02 and 29.44, respectively, and is also visually better than other existing methods. Its 2.64 M parameters effectively balance model efficiency and computational demands.

To address the problems of large storage requirements, computational pressure, untimely data supply of off-chip memory, and low computational efficiency during hardware deployment due to the large number of convolutional neural network (CNN) parameters, we developed an innovative hardware-friendly CNN pruning method called KRP, which prunes the convolutional kernel on a row scale. A new retraining method based on LR tracking was used to obtain a CNN model with both a high pruning rate and accuracy. Furthermore, we designed a high-performance convolutional computation module on the FPGA platform to help deploy KRP pruning models. The results of comparative experiments on CNNs such as VGG and ResNet showed that KRP has higher accuracy than most pruning methods. At the same time, the KRP method, together with the GSNQ quantization method developed in our previous study, forms a high-precision hardware-friendly network compression framework that can achieve “lossless” CNN compression with a 27× reduction in network model storage. The results of the comparative experiments on the FPGA showed that the KRP pruning method not only requires much less storage space, but also helps to reduce the on-chip hardware resource consumption by more than half and effectively improves the parallelism of the model in FPGAs with a strong hardware-friendly feature. This study provides more ideas for the application of CNNs in the field of edge computing.

Motion blur recovery is a common method in the field of remote sensing image processing that can effectively improve the accuracy of detection and recognition. Among the existing motion blur recovery methods, the algorithms based on deep learning do not rely on a priori knowledge and, thus, have better generalizability. However, the existing deep learning algorithms usually suffer from feature misalignment, resulting in a high probability of missing details or errors in the recovered images. This paper proposes an end-to-end generative adversarial network (SDD-GAN) for single-image motion deblurring to address this problem and to optimize the recovery of blurred remote sensing images. Firstly, this paper applies a feature alignment module (FAFM) in the generator to learn the offset between feature maps to adjust the position of each sample in the convolution kernel and to align the feature maps according to the context; secondly, a feature importance selection module is introduced in the generator to adaptively filter the feature maps in the spatial and channel domains, preserving reliable details in the feature maps and improving the performance of the algorithm. In addition, this paper constructs a self-constructed remote sensing dataset (RSDATA) based on the mechanism of image blurring caused by the high-speed orbital motion of satellites. Comparative experiments are conducted on self-built remote sensing datasets and public datasets as well as on real remote sensing blurred images taken by an in-orbit satellite (CX-6(02)). The results show that the algorithm in this paper outperforms the comparison algorithm in terms of both quantitative evaluation and visual effects.

Deep-learning-driven multi-image super-resolution (MISR) reconstruction techniques have significant application value in the field of aerospace remote sensing. In particular, Transformer-based models have shown outstanding performance in super-resolution tasks. However, current MISR models have some deficiencies in the application of multi-scale information and the modeling of the attention mechanism, leading to an insufficient utilization of complementary information in multiple images. In this context, we innovatively propose a Multi-Attention Multi-Image Super-Resolution Transformer (MAST), which involves improvements in two main aspects. Firstly, we present a Multi-Scale and Mixed Attention Block (MMAB). With its multi-scale structure, the network is able to extract image features from different scales to obtain more contextual information. Additionally, the introduction of mixed attention allows the network to fully explore high-frequency features of the images in both channel and spatial dimensions. Secondly, we propose a Collaborative Attention Fusion Block (CAFB). By incorporating channel attention into the self-attention layer of the Transformer, we aim to better establish global correlations between multiple images. To improve the network’s perception ability of local detailed features, we introduce a Residual Local Attention Block (RLAB). With the aforementioned improvements, our model can better extract and utilize non-redundant information, achieving a superior restoration effect that balances the global structure and local details of the image. The results from the comparative experiments reveal that our approach demonstrated a notable enhancement in cPSNR, with improvements of 0.91 dB and 0.81 dB observed in the NIR and RED bands of the PROBA-V dataset, respectively, in comparison to the existing state-of-the-art methods. Extensive experiments demonstrate that the method proposed in this paper can provide a valuable reference for solving multi-image super-resolution tasks for remote sensing.

To address the problems of convolutional neural networks (CNNs) consuming more hardware resources (such as DSPs and RAMs on FPGAs) and their accuracy, efficiency, and resources being difficult to balance, meaning they cannot meet the requirements of industrial applications, we proposed an innovative low-bit power-of-two quantization method: the global sign-based network quantization (GSNQ). This method involves designing different quantization ranges according to the sign of the weights, which can provide a larger quantization-value range. Combined with the fine-grained and multi-scale global retraining method proposed in this paper, the accuracy loss of low-bit quantization can be effectively reduced. We also proposed a novel convolutional algorithm using shift operations to replace multiplication to help to deploy the GSNQ quantized models on FPGAs. Quantization comparison experiments performed on LeNet-5, AlexNet, VGG-Net, ResNet, and GoogLeNet showed that GSNQ has higher accuracy than most existing methods and achieves “lossless” quantization (i.e., the accuracy of the quantized CNN model is higher than the baseline) at low-bit quantization in most cases. FPGA comparison experiments showed that our convolutional algorithm does not occupy on-chip DSPs, and it also has a low comprehensive occupancy in terms of on-chip LUTs and FFs, which can effectively improve the computational parallelism, and this proves that GSNQ has good hardware-adaptation capability. This study provides theoretical and experimental support for the industrial application of CNNs.

Drone and aerial remote sensing images are widely used, but their imaging environment is complex and prone to image blurring. Existing CNN deblurring algorithms usually use multi-scale fusion to extract features in order to make full use of aerial remote sensing blurred image information, but images with different degrees of blurring use the same weights, leading to increasing errors in the feature fusion process layer by layer. Based on the physical properties of image blurring, this paper proposes an adaptive multi-scale fusion blind deblurred generative adversarial network (AMD-GAN), which innovatively applies the degree of image blurring to guide the adjustment of the weights of multi-scale fusion, effectively suppressing the errors in the multi-scale fusion process and enhancing the interpretability of the feature layer. The research work in this paper reveals the necessity and effectiveness of a priori information on image blurring levels in image deblurring tasks. By studying and exploring the image blurring levels, the network model focuses more on the basic physical features of image blurring. Meanwhile, this paper proposes an image blurring degree description model, which can effectively represent the blurring degree of aerial remote sensing images. The comparison experiments show that the algorithm in this paper can effectively recover images with different degrees of blur, obtain high-quality images with clear texture details, outperform the comparison algorithm in both qualitative and quantitative evaluation, and can effectively improve the object detection performance of blurred aerial remote sensing images. Moreover, the average PSNR of this paper’s algorithm tested on the publicly available dataset RealBlur-R reached 41.02 dB, surpassing the latest SOTA algorithm.