{'fraction_non_alphanumeric': 0.01984126984126984, 'fraction_numerical': 0.0, 'mean_word_length': 5.256198347107438, '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}}
The Starlet Transform in Astronomical Data Processing : Application to Source Detection and Image Deconvolution

We begin with traditional source detection algorithms in astronomy. We then introduce the sparsity data model. The starlet wavelet transform serves as our main focus in this article. Sparse modeling, and noise modeling, are described. Applications to object detection and characterization, and to image filtering and deconvolution, are discussed. The multiscale vision model is a further development of this work, which can allow for image reconstruction when the point spread function is not known, or not known well. Bayesian and other algorithms are described for image restoration. A range of examples is used to illustrate the algorithms.