To address the urgent need for precise malaria diagnosis and white blood cell analysis in resource-limited regions, this paper proposes an intelligent blood image analysis system that integrates a portable microscope with deep learning algorithms. The system employs a self-developed high-magnification mobile microscopy platform and adopts an end-cloud-end collaborative architecture to achieve efficient acquisition and remote processing of blood smear images. To cope with the complex distribution of white blood cells and Plasmodium par-asites in the images, we innovatively introduce a multimodal fusion segmentation and feature validation pipeline, efficiently screening candidate Plasmodium regions via a modified AlexNet model, while achieving fine-grained classification of five white blood cell types based on a medically optimized GoogLeNet architecture. Experiments conducted on a real clinical dataset spanning multiple regions and qualities validate the system's superior performance in Plasmodium detection, white blood cell segmentation, and classification tasks, with an overall malaria parasite detection accuracy of 97.74 %, and white blood cell segmentation and classification accuracies of 93.78 % and 94.70 %, respectively, significantly enhancing processing speed and model generalization capability. The results indicate that the system provides efficient and reliable technical support for on-site rapid malaria screening and auxiliary diagnosis of blood diseases, and is expected to be promoted in primary healthcare and mobile diagnostic scenarios.

The phase gradient autofocus (PGA) and its improvements have been aimed to estimate the phase error exclusively from the phase of raw data. In this article, we introduced the Kalman filter (KF) into stripmap PGA (or phase curvature autofocus) by taking advantage of the continuous movement of the aircraft. The fundamental principle is to build a kinematic model and a measurement model to predict the phase curvature of the next subaperture, and to correct the measurement (phase curvature) of the current subaperture. The advantages of employing KF are as follows: 1) the inaccurate PGA estimation due to wrong target selection, serious phase error, or low signal-to-clutter ratio can be corrected by a well-maintained KF; 2) the prediction of the KF can be applied to the data of the next subaperture before phase estimation, to decrease the algorithm converge time, and to increase the estimation accuracy; and 3) KF disciplined PGA naturally fits the sequential processing needs and is capable of generating good phase error estimation in one execution. This helps real-time synthetic aperture radar (SAR) autofocus and motion compensation. The disciplining of the autofocus using KF is not restricted to PGA-based algorithm. It can be applied to other subaperture-based autofocus algorithms.

High-resolution airborne synthetic aperture radar (SAR) systems are very sensible to deviations of the aircraft from the reference track. In high-resolution imagery, the improvement of range resolution increases the difficulty of implementing range cell migration correction (RCMC), while a wider synthetic aperture increases the cumulative time of motion errors which will affect the image quality. To enable accurate motion compensation in image processing, a high-precision navigation system is needed. However, in many cases, due to the limit of accuracy of such systems, motion errors are hard to be compensated correctly, causing mainly the resolution decrease in final image. Moreover, in large swath mode, the range-dependent phase errors are difficult to be compensated by using the conventional autofocus algorithm only. In this paper, we propose a robust motion error estimation method based on raw SAR data. To apply this estimation method, we first estimate the double phase gradients in subaperture. Second, a filtering method based on curve fitting was proposed to reduce the phase estimation errors caused by low signal-to-clutter ratio (SCR). Finally, we propose a weighted total least square method to calculate the motion errors using the filtered phase gradients. Because the proposed algorithm is nonparametric, it can estimate high-order motion errors. This is very important for the airborne SAR, particularly the light aircraft SAR platform, due to their more complicated movement in air turbulence. The versatility that the proposed method can be used in any imaging algorithms is another advantage. The processing of large number of raw SAR data shows that the algorithm is as robust and practical as phase gradient autofocus and can generate better focused images.