An Optimization-Based Approach to Radar Image Reconstruction in Breast Microwave Sensing

Breast microwave sensing (BMS) has been studied as a potential technique for cancer detection due to the observed microwave properties of malignant and healthy breast tissues. This work presents a novel radar-based image reconstruction algorithm for use in BMS that reframes the radar image reconstruction process as an optimization problem. A gradient descent optimizer was used to create an optimization-based radar reconstruction (ORR) algorithm. Two hundred scans of MRI-derived breast phantoms were performed with a preclinical BMS system. These scans were reconstructed using the ORR, delay-and-sum (DAS), and delay-multiply-and-sum (DMAS) beamformers. The ORR was observed to improve both sensitivity and specificity compared to DAS and DMAS. The estimated sensitivity and specificity of the DAS beamformer were 19% and 44%, respectively, while for ORR, they were 27% and 56%, representing a relative increase of 42% and 27%. The DAS reconstructions also exhibited a hot-spot image artifact, where a localized region of high intensity that did not correspond to any physical phantom feature would be present in an image. This artifact appeared like a tumour response within the image and contributed to the lower specificity of the DAS beamformer. This artifact was not observed in the ORR reconstructions. This work demonstrates the potential of an optimization-based conceptualization of the radar image reconstruction problem in BMS. The ORR algorithm implemented in this work showed improved diagnostic performance and fewer image artifacts compared to the widely employed DAS algorithm.

Perceptual Fusion of Electronic Chart and Marine Radar Image

Electronic charts and marine radars are indispensable equipment in ship navigation systems, and the fusion display of these two parts ensures that the vessel can display dangerous moving targets and various obstacles on the sea. To reduce the noise interference caused by external factors and hardware, a novel radar image denoising algorithm using the concept of Generative Adversarial Network (GAN) using Wasserstein distance is proposed. GAN focuses on transferring the image noise distribution between strong and weak noise, while the perceptual loss approach is to suppress the noise by comparing the perceptual characteristics of the output after denoising. Afterwards, an image registration method based on image transformation is proposed to eliminate the imaging difference between the radar image and chart image, in which the visual attribute transfer approach is used to transform images. Finally, the sparse theory is used to process the high frequency and low frequency subband coefficients of the detection image obtained by the fast Fourier transform in parallel to realizing the image fusion. The results show that the fused contour has a high consistency, fast training speed and short registration time.

Reduction of Multiplicative Noise in Radar Images

Introduction. A radar image is an image obtained by remote sensing the earth's surface with a radar device. Radar images are characterized by background graininess caused by speckle noise, which should be filtered to improve the quality of radar images. The structure of speckle noise reduction filters often comprise one or more parameters to control the level of noise smoothing. The values of these parameters have to be selected experimentally. In works devoted to speckle noise filtering, the methods used for selecting filter paraments are rarely clarified.Aim. To present a methodology for selecting the parameters of multiplicative speckle noise filters on a radar image that are optimal in terms of the quality of the resulting image.Materials and methods. The article presents a method for determining the optimal parameters of speckle noise reduction filters. This method was applied to the most conventionally used filters. The search for optimal parameters and testing of the filters were carried out using a specially designed image, which contained the objects most frequently found on radar images. The structural similarity index (SSIM) metric was chosen as a metric that assesses the quality of filtration.Results. After determining the optimal (in terms of SSIM) parameters of speckle noise reduction filters, the filters were compared to select the best filters in terms of the quality of radar image processing. In addition, the operation of the filters under study was tested on images containing various types of objects, namely: large objects, small objects and sharp borders. Knowing which filter copes best with smoothing speckle noise in a particular area and what values of the variable parameters this requires, an optimal quality of radar images can be achieved. Filtering not only improves human perception of radar images, but also reduces the influence of speckle noise during their further processing (object detection, segmentation of areas, etc.).Conclusion. The proposed algorithm allowed optimal parameters for several speckle noise filters to be determined. The quality of filtration was assessed using an expert method (visually) by comparing images before and after filtration, differential images and one-dimensional image slices. The Frost filter and the anisotropic diffusion filter with optimal parameters showed the best processing quality according to the SSIM metric.