{'fraction_non_alphanumeric': 0.015772870662460567, 'fraction_numerical': 0.0, 'mean_word_length': 5.56551724137931, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Inverse Scattering Algorithms Based on Contrast Source Integral Representations

This article reviews a number of algorithms to solve the nonlinear inverse scattering problem, where the discrepancy between the measured data and the predicted data is minimized. These algorithms are all based on a source type of integral representation of the scattered field, where the integral operator acts on a contrast source, being the product of the interior field and the contrast of the scattering object. Special attention is paid to the development of the contrast-source-inversion method, where the contrast sources are updated iteratively, and where, in each iteration, an explicit minimizer for the contrast is obtained. In particular, we discuss the effective implementation of a multiplicative constraint that minimizes the spatial variations of the contrast. We present actual reconstructions from both synthetic and experimental scattered field data.