{'fraction_non_alphanumeric': 0.02033492822966507, 'fraction_numerical': 0.0029904306220095694, 'mean_word_length': 5.219330855018588, '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}}
Detail enhancement of infrared image based on BEEPS

BEEPS(bi-exponential edge-preserving filter) is used to enhance the details of infrared image in this paper. The original infrared image has a dynamic range of 12 or 14 bits, and the human observation range is only 8 bits. Usually, the original infrared image needs to be compressed and displayed by gray-scale remapped for displaying. For example, automatic gain control and histogram equalization are the most widely used image display technologies in infrared imaging systems, but they can lead to the loss of local details, and it is difficult to control the visibility of weak details in images. Therefore, an infrared image digital detail enhancement algorithm has emerged. Current digital enhancement algorithms can effectively enhance image details and avoid over-amplification of noise, but there are still some drawbacks, such as large computational load and poor application flexibility. Therefore, we use BEEPS in our algorithm to overcome these problems. This algorithm uses a two dimensional convolution to separate the detail information from an original infrared image, and turn the original image into the detail layer and the base layer. Detail layer processing is to transform two-dimensional convolution into one-dimensional convolution, and to complete one-dimensional convolution through iterative calculation. Then, the enhanced detail layer is added back to the base frequency layer of histogram equalization. This not only improves the computational efficiency, but also improves the visual quality of the original image. The BEEPS algorithm is proved to be excellent by image and data testing.