Analysis of X-ray and EUV spectra of solar active regions

Data acquired by two flights of an array of six Bragg crystal spectrometers on an Aerobee rocket to obtain high spatial and spectral resolution observations of various coronal features at soft X-ray wavelengths (9-23A) were analyzed. The various aspects of the analysis of the X-ray data are described. These observations were coordinated with observations from the experiments on the Apollo Telescope Mount and the various data sets were related to one another. The Appendices contain the published results, abstracts of papers, computer code descriptions and preprints of papers, all produced as a result of this research project

SDDPM: Speckle Denoising Diffusion Probabilistic Models

Coherent imaging systems, such as medical ultrasound and synthetic aperture radar (SAR), are subject to corruption from speckle due to sub-resolution scatterers. Since speckle is multiplicative in nature, the constituent image regions become corrupted to different extents. The task of denoising such images requires algorithms specifically designed for removing signal-dependent noise. This paper proposes a novel image denoising algorithm for removing signal-dependent multiplicative noise with diffusion models, called Speckle Denoising Diffusion Probabilistic Models (SDDPM). We derive the mathematical formulations for the forward process, the reverse process, and the training objective. In the forward process, we apply multiplicative noise to a given image and prove that the forward process is Gaussian. We show that the reverse process is also Gaussian and the final training objective can be expressed as the Kullback Leibler (KL) divergence between the forward and reverse processes. As derived in the paper, the final denoising task is a single step process, thereby reducing the denoising time significantly. We have trained our model with natural land-use images and ultrasound images for different noise levels. Extensive experiments centered around two different applications show that SDDPM is robust and performs significantly better than the comparative models even when the images are severely corrupted.Comment: 12 pages, 12 figure