Abstract
Outdoor vision systems (OVS) play a vital role in the surveillance of the environment. However, the images and videos captured by these systems could be severely tampered by the sharp intensity changes brought about by adverse weather and climatic conditions. In this work, synthetically prepared rain images are modeled to visualize the randomly distributed rainstreak patterns as noise. The analysis has been performed using various deep learning networks such as auto-encoders with and without skip connections and denoising convolutional neural networks (DnCNN). The best model for this process has been suggested based on mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index (SSIM) obtained by comparing the original and the reconstructed image.
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Akaash, B., Aarthi, R. (2021). An Analysis of Rainstreak Modeling as a Noise Parameter Using Deep Learning Techniques. In: Thampi, S.M., Gelenbe, E., Atiquzzaman, M., Chaudhary, V., Li, KC. (eds) Advances in Computing and Network Communications. Lecture Notes in Electrical Engineering, vol 736. Springer, Singapore. https://doi.org/10.1007/978-981-33-6987-0_38
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DOI: https://doi.org/10.1007/978-981-33-6987-0_38
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