Nanolasers with Feedback as Low-Coherence Illumination Sources for Speckle-Free Imaging: A Numerical Analysis of the Superthermal Emission Regime

Lasers distinguish themselves for the high coherence and high brightness of their radiation, features which have been exploited both in fundamental research and a broad range of technologies. However, emerging applications in the field of imaging, which can benefit from brightness, directionality and efficiency, are impaired by the speckle noise superimposed onto the picture by the interference of coherent scattered fields. We contribute a novel approach to the longstanding efforts in speckle noise reduction by exploiting a new emission regime typical of nanolasers, where low-coherence laser pulses are spontaneously emitted below the laser threshold. Exploring the dynamic properties of this kind of emission in the presence of optical reinjection we show, through the numerical analysis of a fully stochastic approach, that it is possible to tailor some of the properties of the emitted radiation, in addition to exploiting this naturally existing regime. This investigation, therefore, proposes semiconductor nanolasers as potential attractive, miniaturized and versatile future sources of low-coherence radiation for imaging.

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.