Real Time Dehazing System for Automobiles
|© 2020 by IJCTT Journal|
|Year of Publication : 2020|
|Authors : Basil Eldho, Shruti Manmadhan, Susan Jacob, Prof. Joby George HOD|
|DOI : 10.14445/22312803/IJCTT-V68I3P120|
How to Cite?
Basil Eldho, Shruti Manmadhan, Susan Jacob, Prof. Joby George HOD, "Real Time Dehazing System for Automobiles," International Journal of Computer Trends and Technology, vol. 68, no. 3, pp. 99-102, 2020. Crossref, 10.14445/22312803/IJCTT-V68I3P120
Cars have been a common mode of transport ever since their innovation. Fog, smoke and heavy rains pose huge hindrances of sight for people when they drive. This has led to many dangerous accidents especially when it comes to driving at high altitudes or narrow roads. Hence, we propose a real-time image de-hazing system using machine learning and convolutional neural networking concepts. It captures the path in front of the car as video which is then converted to frames and removes all the factors that reduce the clarity of the image. To do so, the loss per pixel is calculated. Here, training sets are utilized in order to obtain better outcomes. Hazed and dehazed images are analyzed and compared and then converted back to dehazed video. It requires a huge refresh rate to make it real time and finally achieve the output.
Convolutional neural networks, machine learning, digital image processing, training data set, layers, dehaze.
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