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.

We propose a new spectral super-resolution technique combined with a Fabry–Perot interferometer (FPI) and an interferometric hyperspectral imager. To overcome the limitation of the maximal optical path difference (OPD) on the spectral resolution, the object spectrum is periodically modulated based on the FPI, and an optical Fourier transform of the modulated spectrum information is performed using a double-beam interferometer to obtain an interferogram. Drawing on the concept of nonlinear structured light microscopy, the displacement of the high-frequency interference information in the interferogram after adding the FPI is analyzed to restore the high-frequency interference information and improve the spectral resolution. The optical system has a compact structure with little impact on complexity, spectral range, or luminous flux. Our simulation results show that this method can realize multicomponent joint-interference imaging to obtain spectral super-resolution information. The effects of the FPI’s reflectance and interval are analyzed, and the reflectance needs to be within 20~80% and the interval must be as close as possible to the maximum optical range of the interferometer. Compared with previous, related innovations, this innovation has the advantages of higher system stability, higher data utilization, and better suitability for interferometric imaging spectrometers.

The application of diffusion models in the field of remote sensing image generation has significantly improved the performance of generation algorithms. However, existing methods still exhibit certain limitations, such as the inability to generate images with rich texture details and minimal geometric distortions in a controllable manner. To address these shortcomings, this work introduces an innovative remote sensing image generation algorithm, OP-Gen, which is guided by textual descriptions and OpenStreetMap (OSM) images. OP-Gen incorporates two information extraction branches: ControlNet and OSM-prompt (OP). The ControlNet branch extracts structural and spatial information from OSM images and injects this information into the diffusion model, providing guidance for the overall structural framework of the generated images. In the OP branch, we design an OP-Controller module, which extracts detailed semantic information from textual prompts based on the structural information of the OSM image. This information is subsequently injected into the diffusion model, enriching the generated images with fine-grained details, aligning the generated details with the structural framework, and thus significantly enhancing the realism of the output. The proposed OP-Gen algorithm achieves state-of-the-art performance in both qualitative and quantitative evaluations. The qualitative results demonstrate that OP-Gen outperforms existing methods in terms of structural coherence and texture detail richness. Quantitatively, the algorithm achieves a Fréchet inception distance (FID) of 45.01, a structural similarity index measure (SSIM) of 0.1904, and a Contrastive Language-Image Pretraining (CLIP) score of 0.3071, all of which represent the best performance among the current algorithms of the same type.

In Bandpass Sampling Fourier Transform Spectroscopy, a comprehensive method for evaluating signal-to-noise ratio (SNR) has not yet been established. This paper employs an energy conservation approach to analyze the relationship of SNR between interferogram and spectra in Bandpass Sampling Time-Modulated Fourier Transform Spectroscopy (BPS-FTS). It systematically presents models for the average SNR of the system interferogram and the average SNR of reconstructed spectra under different parameters. These models are compared with SNR models in traditional Fourier transform spectroscopy (FTS) and are mutually validated through theoretical modeling and simulation analysis. It provides a theoretical basis and simulation verification for the design and implementation of the system.

Multi-frame super-resolution (MFSR) leverages complementary information between image sequences of the same scene to increase the resolution of the reconstructed image. As a branch of MFSR, burst super-resolution aims to restore image details by leveraging the complementary information between noisy sequences. In this paper, we propose an efficient burst-enhanced super-resolution network (BESR). Specifically, we introduce Geformer, a gate-enhanced transformer, and construct an enhanced CNN-Transformer block (ECTB) by combining convolutions to enhance local perception. ECTB efficiently aggregates intra-frame context and inter-frame correlation information, yielding an enhanced feature representation. Additionally, we leverage reference features to facilitate inter-frame communication, enhancing spatiotemporal coherence among multiple frames. To address the critical processes of inter-frame alignment and feature fusion, we propose optimized pyramid alignment (OPA) and hybrid feature fusion (HFF) modules to capture and utilize complementary information between multiple frames to recover more high-frequency details. Extensive experiments demonstrate that, compared to state-of-the-art methods, BESR achieves higher efficiency and competitively superior reconstruction results. On the synthetic dataset and real-world dataset of BurstSR, our BESR achieves PSNR values of 42.79 dB and 48.86 dB, respectively, outperforming other MFSR models significantly.