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A Multi-Scale Gabor Filter-Based Method for Enhancing Video Images in Distance Education

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Abstract

Due to the limited network bandwidth and low-quality end-user devices, the images in distance education videos are transferred in low resolution. It results in low clarity, which badly affects video visibility and leads to poor learning effects. Therefore, a video-enhancing mechanism is required in distance education to ensure quality visualization. The quality is enhanced using a multi-scale Gabor filter, where a color image in the video is converted from the RGB space to the HSI space. A grey-level objective optimization function is constructed to attain a grey-scale image by the gradient conjugate solution method. The attained grey-scale image is used as the basis to obtain the grey-level change information of an image in different directions by using the multi-scale Gabor filter. The image texture is completed and enhanced by using the fractional order differential mask. The local information of the grey-scale images in educational videos is selected and a video image is obtained after the construction of the adaptive mask operator. The experimental results show that the proposed method can effectively grey-scale the images in distance education videos, which improves the sharpness and contrast, and effectively extract the local information. Moreover, the difference between the actual pixels is small, where the maximum difference is 144 and the minimum difference is 55, which shows the extraction results are more accurate.

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We also declare that data availability and ethics approval does not apply to this paper.

References

  1. Chen H, Jin Y, Xu K, Chen Y, Zhu C (2021) Multiframe-to-multiframe network for video denoising. IEEE Trans Multimedia 24:2164–2178

    Article  Google Scholar 

  2. Feng X, Zhang W, Su X, Xu Z (2021) Optical remote sensing image denoising and super-resolution reconstructing using optimized generative network in wavelet transform domain. Remote Sens 13(9):1858

    Article  Google Scholar 

  3. Sarkar S, Bhairannawar SS (2021) Efficient FPGA architecture of optimized Haar wavelet transform for image and video processing applications. Multidimens Syst Signal Process 32(2):821–844

    Article  MathSciNet  Google Scholar 

  4. Guo XG, Xu LJ, Huo JH, Cheng C (2022) Research on image denoising algorithm based on non-local self-similarity. Comput Simul 39(9):364–369475

    Google Scholar 

  5. Sun L, Dong W, Li X, Wu J, Li L, Shi G (2021) Deep maximum a posterior estimator for video denoising. Int J Comput Vision 129:2827–2845

    Article  Google Scholar 

  6. Wu Y, Song W, Zheng J, Liu F (2021) Non-uniform low-light image enhancement via non-local similarity decomposition model. Signal Process Image Commun 93(2):116141

    Article  Google Scholar 

  7. Wang R, Jiang B, Yang C, Li Q, Zhang B (2022) Magan: unsupervised low-light image enhancement guided by mixed-attention. Big Data Min Anal 5(2):110–119

    Article  Google Scholar 

  8. Davy A, Ehret T, Morel JM, Arias P, Facciolo G (2021) Video denoising by combining patch search and cnns. J Math Imaging Vis 63:73–88

    Article  MathSciNet  Google Scholar 

  9. Matin F, Jeong Y, Park H (2020) Retinex-based image enhancement with particle swarm optimization and multi-objective function. IEICE Trans Inf Syst E103(D12):2721–2724

    Article  Google Scholar 

  10. Liu S, Chen P, Woźniak M (2022) Image enhancement-based detection with small infrared targets. Remote Sens 14:3232

    Article  Google Scholar 

  11. Jiang B, Lu Y, Wang J, Lu G, Zhang D (2022) Deep image denoising with adaptive priors. IEEE Trans Circuits Syst Video Technol 32(8):5124–5136

    Article  Google Scholar 

  12. Mahdaoui AE, Ouahabi A, Moulay MS (2022) Image denoising using a compressive sensing approach based on regularization constraints. Sensors 22(6):2199

    Article  Google Scholar 

  13. Xiao Y, Jiang A, Liu C, Wang M (2022) Semantic-aware automatic image colorization via unpaired cycle‐consistent self‐supervised network. Int J Intell Syst 37(2):1222–1238

    Article  Google Scholar 

  14. Kamiyama M, Taguchi A (2021) Color conversion formula with saturation correction from HSI color space to RGB color space. IEICE Trans Fundamentals Electron Commun Comput Sci 104(7):1000–1005

    Article  Google Scholar 

  15. Hassan FS, Gutub A (2022) Improving data hiding within colour images using hue component of HSV colour space. CAAI Trans Intell Technol 7(1):56–68

    Article  Google Scholar 

  16. Chen J, Chen L (2021) Multi-dimensional color image recognition and mining based on feature mining algorithm. Autom Control Comput Sci 55(2):195–201

    Article  Google Scholar 

  17. Chai X, Zhi X, Gan Z, Zhang Y, Chen Y, Fu J (2021) Combining improved genetic algorithm and matrix semi-tensor product (STP) in color image encryption. Sig Process 183:108041

    Article  Google Scholar 

  18. Hua Z, Zhu Z, Chen Y, Li Y (2021) Color image encryption using orthogonal latin squares and a new 2D chaotic system. Nonlinear Dyn 104:4505–4522

    Article  Google Scholar 

  19. Shuai L, Shichen H, Shuai W, Khan M, Paolo B, Javier DS (2023) Visual tracking in complex scenes: a location fusion mechanism based on the combination of multiple visual cognition flows. Inform Fusion 96:281–296

    Article  Google Scholar 

  20. Varghese P, Saroja GAS (2022) Hexagonal image enhancement using Hex-Gabor filter for machine vision applications. Mater Today: Proc 56:555–558

    Google Scholar 

  21. Dakshayani V, Locharla GR, Pławiak P, Datti V, Karri C (2022) Design of a Gabor filter-based image denoising hardware model. Electronics 11(7):1063

    Article  Google Scholar 

  22. Liu J, Fan X, Jiang J, Liu R, Luo Z (2021) Learning a deep multi-scale feature ensemble and an edge-attention guidance for image fusion. IEEE Trans Circuits Syst Video Technol 32(1):105–119

    Article  Google Scholar 

  23. Shuai L, Shichen H, Xiyu X, Jaime L, Khan M (2023) Efficient visual Tracking based on fuzzy inference for Intelligent Transportation Systems. IEEE Trans Intell Transp Syst online first. https://doi.org/10.1109/TITS.2022.3232242

    Article  Google Scholar 

  24. Kumar R, Tripathi R, Marchang N, Srivastava G, Gadekallu TR, Xiong NN (2021) A secured distributed detection system based on IPFS and blockchain for industrial image and video data security. J Parallel Distrib Comput 152:128–143

    Article  Google Scholar 

  25. Rupa C, Harshitha M, Srivastava G, Gadekallu TR, Maddikunta PK (2022) Securing multimedia using a deep learning based chaotic logistic map. IEEE J Biomed Health Inf 27(3):1154–1162

    Article  Google Scholar 

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Acknowledgements

The work was funded by the 2021 National Funding of Production, Education and Research of Chinese Universities - New Research Project of Information Technology (2021ITA02010).

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Yuan Hu provided the algorithm and experimental results and wrote the manuscript and Mahwish Kundi discussed the direction, supervised and analyzed the experiment.

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Correspondence to Mahwish Kundi.

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Hu, Y., Kundi, M. A Multi-Scale Gabor Filter-Based Method for Enhancing Video Images in Distance Education. Mobile Netw Appl 28, 950–959 (2023). https://doi.org/10.1007/s11036-023-02192-w

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