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Edge Enhancement by Noise Suppression in HSI Color Model of UAV Video with Adaptive Thresholding

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A Correction to this article was published on 06 January 2022

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Abstract

UAV video often suffers from noise, blurred edges, fewer details. Quality improvement is essential by noise suppression with an enhancement of edges. Edge detection algorithms for the correct classification of an object and identification have swiftly displayed a system toward processing moving images or whatsoever portion taken from UAVs or drones. To realize it in this paper, we propose a novel algorithm based on HSI transformed domain. Meanwhile, within the paper, we compile some significant analysis within the field of edge enhancement. Relying upon a laboratory evaluation regarding specific proposed techniques for noise suppression with detection of edges and its enhancement process on the UAV video dataset, we confirmed individual contributions, plus our methods’ achievement corresponded over the existent ones. Our proposed Edge Enhancement Algorithm with an HSI color model is the first with better results to the best of our research knowledge. The hybrid algorithm’s effectiveness in dealing with noise reduction working with different videos by finding the SSI, MSE, NRMSE, SNR, PSNR reference-based parameters, and with a reference-less based score, viz. BRISQUE. Our research typically provides a plan to assess the edge with image enhancement from the application’s perspective, encouraging the reformers’ awareness to be evaluated in a particular computer vision context. Results obtained the outcome of the experiment proving that assertion.

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Availability of Data and Material

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

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No funding was received for this research.

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Authors and Affiliations

  1. Department of ITCA, Madan Mohan Malaviya University of Technology, Gorakhpur, India

    Ashish Srivastava & Jay Prakash

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  1. Ashish Srivastava
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  2. Jay Prakash
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Correspondence to Ashish Srivastava.

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The authors declare that they have no known competing conflicts of interest.

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The code of the current study are not publicly available due to the ongoing research as a part of future work of this version but are available from the corresponding author on reasonable request.

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The original version of this article was revised: Four missing references were added: Burg, A. P., Keller, R., Wassner, J., Felber, N. & Fichtner, W. (2000) A 3D-DCT real-time video compression system for low complexity single-chip VLSI implementation. Proc. of the Mobile Multimedia Conference. Lee, M. C., Chan, R. K. W. & Adjeroh, D. A. (1997) Quantization of 3D-DCT coefficients and scan order for video compression. Journal of Visual Communication and Image Representation 8(4), 405-422. Rong, W., Li, Z., Zhang, W. & Sun, L. (2014) An improved CANNY edge detection algorithm. IEEE International Conference on Mechatronics and Automation, 577-582. Yan, F. & Chen, D.-F. (2013) Video reconstruction via online compressed sensing. Fifth International Conference on Digital Image Processing (ICDIP 2013), 88780J. International Society for Optics and Photonics.

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Srivastava, A., Prakash, J. Edge Enhancement by Noise Suppression in HSI Color Model of UAV Video with Adaptive Thresholding. Wireless Pers Commun 124, 163–186 (2022). https://doi.org/10.1007/s11277-021-09334-x

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