{'fraction_non_alphanumeric': 0.01822079314040729, 'fraction_numerical': 0.0, 'mean_word_length': 5.970149253731344, '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}}
Superresolution of Noisy Remotely Sensed Images Through Directional Representations

We develop an algorithm for single-image superresolution of remotely sensed data based on the discrete shearlet transform. The shearlet transform extracts directional features of signals and is known to provide near-optimally sparse representations for a broad class of images. This often leads to superior performance in edge detection and image representation when compared with isotropic frames. We justify the use of shearlets mathematically, before presenting a denoising single-image superresolution algorithm that combines the shearlet transform with sparse mixing estimators (SMEs). Our algorithm is compared with a variety of single-image superresolution methods, including wavelet SME superresolution. Our numerical results demonstrate competitive performance in terms of peak-signal-to-noise ratio and structural similarity index metric.