Saiwa

  As the usage of digital cameras and cell phones develops, people are exposed to a wider range of images in their daily lives. Some of the photographs are high-quality, while others are not. Image quality suffers when there is noise present. This noise might be caused by low light levels or other difficulties with intensity. There are various online ways for picture denoising or image denoising. It has long been a prominent area of study, and it is currently being researched by professionals. We will look at how deep learning algorithms are used to denoise a picture in this part. What is Noise? Noise is widely characterized as a random shift in brightness or color information, and it is typically produced by the picture capture sensor's technological limits or bad environmental circumstances. Image noise is a prevalent issue that must be handled with good image-denoising algorithms since it is inescapable in real-world circumstances. During picture acquisition and transmissi

  Anomaly detection is a critical issue that has been studied in a variety of academic and practical fields. Many outlier detection approaches are customized to certain application domains, while others are more generic. This article aims to provide a comprehensive and in-depth overview of works on outlier detection methodologies, with a particular emphasis on anomaly detection in the setting of advanced learning and surface defect detection. What is anomaly detection? The technique of recognizing out-of-the-ordinary features or occurrences in data sets is known as anomaly detection . it is frequently done on unsupervised learning, which would be known as "unsupervised anomaly detection". It is a prominent study issue in the field of machine learning, to discriminate between normal and aberrant samples in a dataset. Many algorithms for detecting anomalies have been developed expressly for certain application areas, while others are more general.   What are the differ