Abstract
Low visibility causes haze in images due to fog or dust particles in the atmosphere. The haze causes color distortion and even blurring in the images captured. Machine learning approach has been considered to provide optimized haze removal results to generate higher quality images from where information can be extracted. In this context, machine learning-based random forest regressor algorithm along with post-processing techniques was proposed as a superior solution for de-hazing images and thereby generating higher quality images in comparison to other direct de-hazing methods.
Highlights
The machine learning algorithms are computed from previous data and are unique because it does not rely on predetermined equations models
The machine learning process is quite efficient in the dehazing methods as it enables analysis of massive data quantities faster and of high quality meaning that it is minimal or no loss of information in the images.The learning based algorithm based on the random forest is better in comparison to the Photoshop plugin that is considered to be state of the art
The algorithms major problem is the difficulty in getting proper training information, and most of the data must first undergo synthesis for it to be used,butotherwise, it is the best de-hazing method
Summary
Haze is a significant problem in the imaging industry, which results in reduced visibility of image details [1]. Many methods of eliminating the haze have been developed and lately, machine learning techniques have been introduced as alternative ways to achieve optimal dehazing. Machine learning is the process of teaching machines what to do and learn from past experiences. The machine learning algorithms are computed from previous data and are unique because it does not rely on predetermined equations models. Machine learning has come a long way in the past two decades, enabling the achievement of self-driving cars and even things as simple and essential as an efficient web search [2]. Machine learning is a crucial factor in the de-hazing of images as it has made milestones in image processing and computer vision that is accompanied by motion and object detection
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