{'fraction_non_alphanumeric': 0.020664869721473494, 'fraction_numerical': 0.0035938903863432167, 'mean_word_length': 5.329545454545454, '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}}
[papers] General Improvement Method of Specular Component Separation Using High-Emphasis Filter and Similarity Function

Separating reflection components is a fundamental problem in computer vision and useful for many applications such as image quality. We propose a novel method that improves the accuracy of separating reflection components from a single image. Although several algorithms for separating reflection components have been proposed, our method can additionally improve the accuracy based on their results. First, we obtain diffuse and specular components by using an existing algorithm. Then, we apply a high-emphasis filter for each component. Since the responses of the high-emphasis filter where the separation fails become larger than the original values, we can detect erroneous pixels. Thus, we replace separation results of these erroneous pixels with results of other reference pixels from the image considering the similarity between the target and reference pixels. Experimental results show that our method can improve at most 13.61 dB in terms of the Peak Signal-to-Noise Ratio (PSNR).