Accurate cell segmentation remains challenging due to morphological variations, diverse imaging modalities, and unclear cellular boundaries. Existing deep learning (DL) methods struggle to extract features adaptively across heterogeneous cellular environments, thereby limiting generalization capacity. To address these challenges, we propose FlexiCell, a novel adaptive segmentation framework that integrates a learnable adaptive filter with dual attention mechanisms. The core innovation lies in the FlexiFilter approach, which combines standard convolution with adaptive residual learning through learnable mixing parameters. These parameters dynamically balance input preservation and feature enhancement. FlexiCell employs multi-scale FlexiFilter blocks with varying kernel sizes, channel and spatial attention networks, and a dedicated boundary extractor for precise edge detection. Extensive experiments demonstrate superior performance compared to benchmark models, achieving 3.8% improvement in detection accuracy and 5.5 % in segmentation quality on our newly developed induced pluripotent stem (iPS) cell datasets. Further evaluation on standardized Cell Tracking Challenge (CTC) benchmarks confirms state-of-the-art performance on mesenchymal stem cells and glioblastoma datasets, outperforming established CTC methods. The framework demonstrates robust generalization across fluorescence, phase contrast, and differential interference contrast microscopy, without requiring dataset-specific optimization. Codes are available at https://github.com/jovialniyo93/FlexiCell.

Synthetic aperture radar (SAR) is vulnerable to interference, including intentional and unintentional-ones. Noise frequency modulated (FM) interference is a kind of intentional interference, which has the characteristics of broadband and randomness, which makes the noise FM signal become a kind of most commonly used interference signal. Noise FM interference will have a serious impact on the SAR image, but the current algorithms for interference suppression are not sufficiently studied. This paper extends a time-domain cancellation algorithm for suppressing the noise FM interference of SAR. This algorithm can reconstruct the noise FM interference signal from the contaminated SAR echo, and then suppress the interference component in the echo by time-domain cancellation. Finally, this paper validates the superior performance of the algorithm by point target simulation and Radarsat-1 data. The proposed method is valid even when the signal-to-interference ratio is lower than -40dB.

Super resolution technology has played a significant role in enhancing the imaging resolution of forward-looking scanning radar. However, a large number of super-resolution methods still rely on single frame scanning echoes. This paper aims to leverage multi-frame real beam images for super-resolution imaging, utilizing the complementary information present in multiple images to construct a higher resolution image. This paper first establishes the multi-frame super-resolution imaging model. Subsequently, a feasible multi-frame super-resolution method was proposed, and motion parameter estimation was performed using the correlated phase method. Finally, the effectiveness of the proposed method was verified through simulation experiments.

The signal emitted by ground radiation sources often interferes with Synthetic Aperture Radar (SAR) satellites, with the most common interference being periodic pulses emitted by ground radars. This paper proposes a method for locating ground-based periodic pulse signal interference sources using SAR echo data. Firstly, We estimate the Time Difference of Arrival (TDOA) of each pulse emitted by the interference source to SAR from the received SAR signals, and we seek the mapping relationship between the coordinates of the interference source (latitude and longitude) and the variations in TDOA. Using this mapping relationship, we achieve the localization of the interference source through a two-dimensional search method. The proposed method in this paper is highly versatile, applicable to single-station SAR satellites, multi-station SAR, and single-station SAR with multiple passes. It is also applicable regardless of the modulation form of the interference signal. Finally, the proposed TDOA-based localization method is experimentally validated for its accuracy based on GaoFen-3 satellite-borne SAR. The results demonstrate that the positioning error using two measurements from the satellite is only 3.708 km.