{'fraction_non_alphanumeric': 0.02268041237113402, 'fraction_numerical': 0.0006872852233676976, 'mean_word_length': 5.358078602620087, '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}}
Improving resolution of images using Generative Adversarial Networks

Even with all the achievements in precision and speed of various image super-resolution models, such as better and more accurate Convolutional Neural Networks (CNN), the results have not been satisfactory. The high-resolution images produced are generally missing the finer and frequent texture details. The majority of the models in this area focus on such objective functions which minimize the Mean Square Error (MSE). Although, this produces images with better Peak Signal to Noise Ratio (PSNR) such images are perceptually unsatisfying and lack the fidelity and high-frequency details when seen at a high-resolution. Generative Adversarial Networks (GAN), a deep learningmodel, can be usedfor such problems. In this article, the working of the GAN is shown and described about the production satisfying images with decent PSNR score as well as good Perceptual Index (P1) when compared to other models. In contrast to the existing Super Resolution GAN model, various modifications have been introduced to improve the quality of images, like replacing batch normalization layer with weight normalization layer, modified the dense residual block, taking features for comparison before they are fed in activation layer, using the concept of a relativistic discriminator instead of a normal discriminator that is used in vanilla GAN and finally, using Mean Absolute Error in the model.