Open AccessImage Dehazing from Repeated Averaging Filters with Artificial Neural Network
Author(s) -
V.Francis Densil Raj S.Sanjeeve Kumar,
Madhu Lata Nirmal
Publication year - 2022
Publication title -
international journal for research in applied science and engineering technology
Language(s) - English
Resource type - Journals
ISSN - 2321-9653
DOI - 10.22214/ijraset.2022.40484
Subject(s) - computer science , artificial intelligence , smoothing , computer vision , image (mathematics) , filter (signal processing) , image processing , artificial neural network , gaussian blur , signal (programming language) , process (computing) , digital image processing , image restoration , pattern recognition (psychology) , programming language , operating system
The physical process of converting an image signal into a physical image is known as image processing. Either a digital or analogue image signal can be used. An actual physical image or the attributes of an image can be the actual output. The logical process of detecting, identifying, classifying, measuring, and evaluating the relevance of physical and cultural items, their patterns, and spatial relationships is referred to as image processing. This paper presents a method for measuring ambient light from a single foggy image using Repeated Averaging Filters, which adds to greater radiance recovery. The problem of halo artefacts in the final output image after dehazing plagues existing dehazing algorithms. An averaged channel is created from a single image using recurrent averaging filters using integral images with artificial neural network, which is a faster and more efficient method of reducing halo artefacts. In terms of quantitative and computational analysis, the suggested dehazing method achieves competitive results and outperforms many earlier state-of-the-art solutions. Index Terms: Image Dehazing; Averaging Filter; Integral Image; Gaussian smoothing, Feed Forward Neural Network