Image dehazing using autoencoder convolutional neural network

In hazy weather, the image in the scene suffers from noise which makes them less visible and to detect an object in hazy weather becomes a challenging task in computer vision. To have noise free image, many researchers have devised denoising techniques for enhancing visibility of images. Denoising i...

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Bibliographic Details
Published in:International journal of system assurance engineering and management Vol. 13; no. 6; pp. 3002 - 3016
Main Authors: Singh, Richa, Dubey, Ashwani Kumar, Kapoor, Rajiv
Format: Journal Article
Language:English
Published: New Delhi Springer India 01.12.2022
Springer Nature B.V
Subjects:
Artificial neural networks
Computer vision
Datasets
Engineering
Engineering Economics
Haze
Image classification
Image enhancement
Image quality
Logistics
Machine learning
Marketing
Neural networks
Noise reduction
Organization
Original Article
Quality Control
Reliability
Safety and Risk
Visibility
Weather
Deep neural network
Neural network
Autoencoder
Rectified linear unit
Convolutional neural network
ISSN:0975-6809, 0976-4348
Online Access:Get full text
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Summary:In hazy weather, the image in the scene suffers from noise which makes them less visible and to detect an object in hazy weather becomes a challenging task in computer vision. To have noise free image, many researchers have devised denoising techniques for enhancing visibility of images. Denoising is to remove the random variation from images and preserve the image features. As hazy images cause lots of visibility issues, this paper proposes removing haze and enhancing visibility of bad weather images with improved efficacy using an unsupervised neural network autoencoder that compress the data using machine learning and learns through Convolutional Neural Network (CNN). It has been observed that to have increased accuracy, the image classification and analysis is most effective using CNN. An end-to-end decoder training model is used to achieve the quality images. Further, various optimizers are compared to have better accuracy. The quality of images identified by estimation of performance such as RMSE and PSNR values are evaluated over single image and images from existing datasets and our own dataset. In the proposed method, RMSE value comes out to be 0.0373 for image from BSD500 dataset for specific image compared with other state of art approaches. The proposed model is intended in addition to other active, or progressive methods and the suggested method exceeds. The performance quality of images is explored applying measurable metrics. The images are taken from the datasets O-Haze, I-Haze, BSDS500, RESIDE, FRIDA and some from google.
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ISSN:0975-6809
0976-4348
DOI:10.1007/s13198-022-01780-5