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A new deep learning architecture for dehazing of aerial remote sensing images

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Abstract

A major problem in most aerial remote image processing applications is the presence of haze in images. It is a phenomenon by which particles in the atmosphere disperse light, thus altering the quality of the overall image. This can be detrimental to the performance of vision-based algorithms such as those concerned with object detection. There have been numerous attempts using traditional image processing techniques as well as using deep learning approaches to eliminate this haze. In most cases, models tend to make assumptions on the nature of haze that are rarely true in reality. In this paper, we propose an end-to-end deep learning architecture that can dehaze aerial remote sensing images efficiently with minimal deviation from the ground truth. Many of the assumptions made in other models are eliminated and the relationship between hazed and dehazed images is directly computed. The proposed model is based on the observation that identifying structural and statistical portions separately from an image and using those features to reconstruct the image can give a realistic dehazed image. It also makes use of information exposed by different color spaces to achieve this using lesser computation. The experimental quantitative and qualitative results of the proposed architecture are compared with recent benchmark dehaze models on NYU hazy dataset and real-world hazy images. Experimental results yield that the proposed architecture outperforms benchmark models on test aerial remote sensing images.

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Acknowledgment

This publication is the outcome of R & D work undertaken in the Young Faculty Research Fellowship project under Visvesvaraya Ph.D. Scheme of Ministry of Electronics & Information Technology (MeitY), Government of India in the National Institute of Technology Karnataka, Surathkal being implemented by Digital India Corporation (Formerly Media Lab Asia), New Delhi, Grant No. DIC/MUM/GA/10(37)D, Dated 24-01-2019.

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  1. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal Mangaluru, 575025, Karnataka, India

    Abhi Kalra, Aaron Sequeira, Aditya Manjunath, Shyam Lal & Raghavendra BS

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  1. Abhi Kalra
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  2. Aaron Sequeira
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  3. Aditya Manjunath
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  5. Raghavendra BS
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Correspondence to Shyam Lal.

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Kalra, A., Sequeira, A., Manjunath, A. et al. A new deep learning architecture for dehazing of aerial remote sensing images. Multimed Tools Appl 81, 43639–43655 (2022). https://doi.org/10.1007/s11042-022-13122-5

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