DEEP LEARNING AND GLOBALLY GUIDED IMAGE FILTERING TECHNIQUE BASED IMAGE DEHAZING AND ENHANCEMENT
Sangeeta Rathi, Savita Bamal · International Journal of Technical Research & Science · 2020
Haze in images is due to natural environmental phenomena, which makes the image in a white shade noise.Haze removal is one of the most important research topics these days due to popularity of applications in the real surveillance from drones or any area under security.Both indoor and outdoor images are important for testing haze and its removal.Many image processing techniques are made by researchers to remove haze in a single image.Haze intensity can be calculated by a parameter known as perceptual fog density (PFD).It is important to analyze this parameter for all the techniques so as to get an idea of improvement.In this paper, a new approach is made by applying globally guided filtering technique with deep neural network.This proposed algorithm is implemented on MATLAB software and results are obtained by calculating PFD in the existing and proposed technique.The four techniques are compared with each other.The techniques are global filtering (GIF), weighted global filtering (WGIF), globally guided filtering (GGIF) and proposed technique i.e. globally guided filtering with DNN (Deep Neural Network).In GIF, the fine structure of the image is generally not preserved, but in proposed algorithm, the PDF is the minimum with fine structure, color intensity of the picture is of the best quality.