A Multi-scale Generalized Shrinkage Threshold Network for Image Blind Deblurring in Remote Sensing

Remote sensing images are essential for many earth science applications, but their quality can be degraded due to limitations in sensor technology and complex imaging environments. To address this, various remote sensing image deblurring methods have been developed to restore sharp, high-quality images from degraded observational data. However, most traditional model-based deblurring methods usually require predefined hand-craft prior assumptions, which are difficult to handle in complex applications, and most deep learning-based deblurring methods are designed as a black box, lacking transparency and interpretability. In this work, we propose a novel blind deblurring learning framework based on alternating iterations of shrinkage thresholds, alternately updating blurring kernels and images, with the theoretical foundation of network design. Additionally, we propose a learnable blur kernel proximal mapping module to improve the blur kernel evaluation in the kernel domain. Then, we proposed a deep proximal mapping module in the image domain, which combines a generalized shrinkage threshold operator and a multi-scale prior feature extraction block. This module also introduces an attention mechanism to adaptively adjust the prior importance, thus avoiding the drawbacks of hand-crafted image prior terms. Thus, a novel multi-scale generalized shrinkage threshold network (MGSTNet) is designed to specifically focus on learning deep geometric prior features to enhance image restoration. Experiments demonstrate the superiority of our MGSTNet framework on remote sensing image datasets compared to existing deblurring methods.Comment: 12 pages

Over-the-Air Testing for Connecting Faults Diagnosis in Beamforming Antenna Arrays with Short Measurement Distance

A novel diagnosis method for detecting the connecting faults (i.e., disconnected and misconnected antenna elements) in beamforming antenna array is proposed. Compared with state-of-the-art methods, the proposed diagnosis method can be conducted when the phased array operates in its default beam-steering mode. Moreover, the proposed diagnosis method is fast since it only requires a few near-field measurement positions in a very short distance (i.e., the near-field of the array). Measurement uncertainties, e.g., the scatterings from the practical testing environment, are considered in the method. Therefore, the proposed method can robustly detect beamforming array connecting faults in practical production line testing environments. The method is first validated using an 11-element dual-polarized base station (BS) antenna array at 2.7 GHz by numerical simulations. It is further experimentally validated using an eight-element single-polarized patch antenna array at 3.6 GHz. The same antenna array also serves as the probe array with only a 10-cm distance between the antenna under test (AUT) and the probe array. The diagnosis results for different types of connecting faults with numerical simulations and measurement validations have verified the effectiveness and robustness of the proposed method in practical applications.</p