A Review on Computational Low-Light Image Enhancement Models: Challenges, Benchmarks, and Perspectives

Pre-processing techniques such as low-light image improvement have a wide variety of practical uses. Enhancing optical acuity and the caliber of photos taken in low-light are the objectives. Techniques for improving low-light images simultaneously boost the brightness, contrast, as well as noise reduction of the image. Self-learning tools, however, have accelerated a lot of this field advancements. Many deep neural networks have been created or put into use as a result. As such, this paper gives a quick summary of the state of the art in low-light image improvement, encompassing techniques related to the controversial open subject. We present a summary of deep learning techniques that are currently carried out to low-light settings. A clear overview of traditional methods for improving low-light primary images. We provide enhanced techniques based on deep learning algorithms and neural structure topologies. Specifically, the current state of deep learning -based low-light picture improvement technologies may be broadly categorized into four sections: visually-based approaches, unobserved learning, unsupervised learning, and observational learning technologies. After then, a database of dimly lit photos is gathered and examined. Furthermore, we present an overview of several quality evaluation standards for enhancing low-light images.