{'fraction_non_alphanumeric': 0.013910355486862442, 'fraction_numerical': 0.0, 'mean_word_length': 5.442786069651741, '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}}
DPCB-CycleGAN: a heterologous image migration algorithm based on attention mechanism and CycleGAN

Infrared images have significant thermal radiation information and high penetration power and are unaffected by illumination. However, the current reality is that infrared images are more challenging to obtain than visible images. The importance of large numbers of paired heterogeneous images is particularly evident in vision tasks such as image fusion. Therefore, it is essential to expand and migrate infrared images using visible images. In this paper, we propose an attentionbased mechanism and CycleGAN heterogenous image migration algorithm which is termed DPCB-CycleGAN. It removes the traditional convolution operation of the generator and combines CBAM with depthwise separable convolution. Feature extraction of infrared and visible source images in both spatial and channel dimensions, increasing the weight of features in salient regions and learning the laws of migration for both types of images, making the generated images closer to the real ones. The method is trained and tested on the TNO public dataset, and the experimental results show it is effective. It has achieved satisfactory results from both a quantitative and qualitative point of view and is very generalizable.