CSIDNet: Compact single image dehazing network for outdoor scene enhancement
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CSIDNet: Compact single image dehazing network for outdoor scene enhancement Teena Sharma1
· Isha Agrawal2 · Nishchal K. Verma1
Received: 18 April 2020 / Revised: 19 July 2020 / Accepted: 29 July 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper proposes a novel deep learning-based single image dehazing network named as Compact Single Image Dehazing Network (CSIDNet) for outdoor scene enhancement. CSIDNet directly outputs a haze-free image from the given hazy input. The remarkable features of CSIDNet are that it has been designed only with three convolutional layers and it requires lesser number of images for training without diminishing the performance in comparison to the other commonly observed deep learning-based dehazing models. The performance of CSIDNet has been analyzed on natural hazy scene images and REalistic Single Image DEhazing (RESIDE) dataset. RESIDE dataset consists of Outdoor Training Set (OTS), Synthetic Objective Testing Set (SOTS), and real-world synthetic hazy images from Hybrid Subjective Testing Set (HSTS). The performance metrics used for comparison are Peak Signal to Noise Ratio (PSNR) and Structural SIMilarity (SSIM) index. The experimental results obtained using CSIDNet outperform several well known state-of-the-art dehazing methods in terms of PSNR and SSIM on images of SOTS and HSTS from RESIDE dataset. Additionally, the visual comparison shows that the dehazed images obtained using CSIDNet are more appealing with better edge preservation. Since the proposed network requires minimal resources and is faster to train along with lesser run-time, it is more practical and feasible for real-time applications. Keywords Outdoor scene enhancement · Dark channel · Illumination channel · Convolutional neural networks · Residual learning
Teena Sharma
[email protected] Isha Agrawal [email protected] Nishchal K. Verma [email protected] 1
Department of Electrical Engineering IIT Kanpur, Kanpur, India, 208016
2
Department of Computer Science and Engineering IIITDM Jabalpur, Jabalpur, India, 482005
Multimedia Tools and Applications
1 Introduction Various particulate matters such as dust, water drops, aerosol, etc. in the atmosphere often obscure the clarity of vision-based applications in outdoor environment. The most common phenomenon in outdoor environment due to inclement weather conditions is haze [25]. With a rise in the number of vision-based applications such as object classification [14], autonomous driving [31], remote sensing [5], etc., outdoor scene enhancement has become increasingly desirable for obtaining a clear scene. In literature, various methodologies have been developed to resolve the same.
1.1 Motivation Haze is a signal dependent non-linear noise. This incurs attenuation in an image with increase in scene depth [25]. Thus, the pixel locations in a scene image suffer different amount of degradation. Single image dehazing has gained more popularity lately than those requiring additional data like multiple images
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