files/journal/2022-09-02_12-20-40-000000_622.png

International Journal of Soft Computing

ISSN: Online
ISSN: Print 1816-9503
About US
Editors
Issues
Pages
Indexed in

International Journal of Soft Computing, Volume 17, Issue 3 (2022)

References

  1. Ahmad, I. and K. Pothuganti, 2020. Design & Implementation of Real Time Autonomous Car by Using Image Processing & IoT. Third International Conference on Smart Systems and Inventive Technology (ICSSIT), August 20-22, 2020, IEEE, India, pp: 107-113.
  2. Aburaed, N., A. Panthakkan, M. Al-Saad, M.C.E. Rai, S.A. Mansoori, H. Al-Ahmad and S. Marshall, 2020. Super-resolution of satellite imagery using a wavelet multiscale-based deep convolutional neural network model. Image Signal Process. Remote Sens. XXVI, Vol. 11533. 10.1117/12.2573991.
  3. Yamashita, K. and K. Markov, 2020. Medical Image Enhancement Using Super Resolution Methods. In: Lecture Notes in Computer Science., Krzhizhanovskaya, V.V., G. Závodszky, M.H. Lees, J.J. Dongarra, P.M.A. Sloot, S. Brissos and J. Teixeira, (Eds.)., Springer International Publishing, Springer, Cham, ISBN-27: 9783030504250,9783030504267, pp: 496-508.
  4. Farooq, M.A., A.A. Khan, A. Ahmad and R.H. Raza, 2021. Effectiveness of state-of-the-art super resolution algorithms in surveillance environment. Digital Interaction Machine Intell., Vol. 2021. 10.1007/978-3-030-74728-2_8.
  5. Iftenea, M., Q. Liub and Y. Wangc, 2017. Very high resolution images classification by fusing deep convolutional neural networks. Very High Resolution Images Classification by Fusing Deep Convolutional Neural Networks., https://www.clausiuspress.com/conferences/ACSS/ACSAT%202017/GACS57.pdf.
  6. Wang, L., D. Li, Y. Zhu, L. Tian and Y. Shan, 2020. Dual Super-Resolution Learning for Semantic Segmentation. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, IEEE, USA, pp: 3773-3782.
  7. Gómez, D. and A. Rojas, 2016. An empirical overview of the no free lunch theorem and its effect on real-world machine learning classification. Neural Comput., 28: 216-228.
  8. Blau, Y., R. Mechrez, R. Timofte, T. Michaeli and L. Zelnik-Manor, 2019. The 2018 PIRM challenge on perceptual image super-resolution. Proceedings of the European Conference on Computer Vision (ECCV) Workshops, https://arxiv.org/abs/1809.07517.
  9. Yang, W., X. Zhang, Y. Tian, W. Wang, J.H. Xue and Q. Liao, 2019. Deep learning for single image super-resolution: A brief review. IEEE Trans. Multimedia, 21: 3106-3121.
  10. Kopf, J. and D. Lischinski, 2011. Depixelizing pixel art. ACM Trans. Graphics, 30: 1-8.
  11. Choi, J.S. and M. Kim, 2017. A deep convolutional neural network with selection units for super-resolution. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, IEEE, USA, pp: 1150-1156.
  12. Sun, L. and J. Hays, 2012. Super-Resolution From Internet-Scale Scene Matching. IEEE International Conference on Computational Photography (ICCP), April 28-29, 2012, Seattle, USA, pp: 1-12.
  13. Cheng, X. and Z. Chen, 2020. Video frame interpolation via deformable separable convolution. Proc. AAAI Conf. Artif. Intell., 34: 10607-10614.
  14. Laghrib, A., A. Hadri, A. Hakim and S. Raghay, 2019. A new multiframe super-resolution based on nonlinear registration and a spatially weighted regularization. Inform. Sci., 493: 34-56.
  15. Farsiu, S., M.D. Robinson, M. Elad and P. Milanfar, 2004. Fast and robust multiframe super resolution. IEEE Trans. Image Process., 13: 1327-1344.
  16. Chen, H., X. He, L. Qing, Y. Wu, C. Ren, R.E. Sheriff and C. Zhu, 2022. Real-world single image super-resolution: A brief review. Inform. Fusion, 79: 124-145.
  17. Zhou, F., W. Yang and Q. Liao, 2012. Interpolation-based image super-resolution using multisurface fitting. IEEE Trans Image Process., 21: 3312-3318.
  18. Hardie, R.C., K.J. Barnard and E.E. Armstrong, 1997. Joint MAP registration and high resolution image estimation using a sequence of undersampled images. IEEE Trans. Image Process., 6: 1621-1633.
  19. Dong, C., C.C. Loy, K. He and X. Tang, 2016. Image super-resolution using deep convolutional networks. IEEE Trans. Pattern Anal. Machine Intell., 38: 295-307.
  20. Yang, C.Y., C. Ma and M.H. Yang, 2014. Single-Image Super-Resolution: A Benchmark. In: European Conference on Computer Vision, Fleet, D., T. Pajdla, B. Schiele and T Tuytelaars, (Eds.)., Springer International Publishing, Cham, ISBN-27: 9783319105925,9783319105932, pp: 372-386.
  21. Yang, J., J. Wright, T.S. Huang and Y. Ma, 2010. Image super-resolution via sparse representation. IEEE Trans. Image Process., 19: 2861-2873.
  22. Zhang, K., J. Liang, L.V. Gool and R. Timofte, 2021. Designing a Practical Degradation Model for Deep Blind Image Super-Resolution Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), October 10-17, 2021, IEEE, Canada, pp: 4791-4800.
  23. Schulter, S., C. Leistner and H. Bischof, 2015. Fast And Accurate Image Upscaling With Super-Resolution Forests. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 07-12, 2015, IEEE, USA, pp: 3791-3799.
  24. Johnson, J., A. Alahi and L. Fei-Fei, 2016. Perceptual Losses for Real-Time Style Transfer and Super-Resolution. In: Computer Vision – ECCV 2016. ECCV 2016, Leibe, B., J. Matas, N. Sebe and M. Welling (Eds.)., Springer International Publishing, Cham, ISBN-27: 9783319464749,9783319464756, pp: 694-711.
  25. Lim, B., S. Son, H. Kim, S. Nah and K.M. Lee, 2017. Enhanced deep residual networks for single image super-resolution. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, https://openaccess.thecvf.com/content_cvpr_2017_workshops/w12/papers/Lim_Enhanced_Deep_Residual_CVPR_2017_paper.pdf.
  26. Ledig, C., L. Theis, F. Huszar, J. Caballero and A. Cunningham et al., 2017. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, IEEE, Honolulu, pp: 105-114.
  27. Zhang, K., D. Li, W. Luo, W. Ren and B. Stenger et al., 2021. Benchmarking Ultra-High-Definition Image Super-Resolution. IEEE/CVF International Conference on Computer Vision (ICCV), October 10-17, 2021, IEEE, Canada, pp: 14749-14758.
  28. Wang, Z., J. Chen and S.C.H. Hoi, 2021. Deep learning for image super-resolution: A survey. IEEE Trans. Pattern Anal. Machine Intell., 43: 3365-3387.
  29. Liu, H., Z. Ruan, P. Zhao, C. Dong and F. Shang et al., 2022. Video super-resolution based on deep learning: A comprehensive survey. Artif. Intell. Rev., 55: 5981-6035.
  30. Anwar, S. and N. Barnes, 2022. Densely residual laplacian super-resolution. IEEE Trans. Pattern Anal. Machine Intell., 44: 1192-1204.
  31. Zeyde, R., M. Elad and M. Protter, 2012. On Single Image Scale-Up Using Sparse-Representations. In: Curves and Surfaces., Boissonnat, J.D., P. Chenin, A. Cohen, C. Gout, T. Lyche, M.L. Mazure and L. Schumaker, (Eds.)., Springer Berlin Heidelberg, Berlin, Heidelberg, ISBN-27: 9783642274121,9783642274138, pp: 711-730.
  32. Martin, D., C. Fowlkes, D. Tal and J. Malik, 2001. A Database of Human Segmented Natural Images and its Application to Evaluating Segmentation Algorithms and Measuring Ecological Statistics. Proceedings Eighth IEEE International Conference on Computer Vision, July 07-14, 2001, IEEE Comput. Soc, Canada, pp: 416-423.
  33. Huang, J.B., A. Singh and N. Ahuja, 2015. Single Image Super-Resolution from Transformed Self-Exemplars. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 07-12, 2015, IEEE, Boston, pp: 5197-5206.
  34. Aizawa, K., A. Fujimoto, A. Otsubo, T. Ogawa, Y. Matsui, K. Tsubota and H. Ikuta, 2020. Building a manga dataset “Manga109” with annotations for multimedia applications. IEEE MultiMedia, 27: 8-18.
  35. Agustsson, E. and R. Timofte, 2017. NTIRE 2017 challenge on single image super-resolution: Dataset and study. IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, IEEE, USA, pp: 1122-1131.
  36. Clifton, A. and I.I. Ericson, 2005. Hazard Analysis Techniques for System Safety. John Wiley & Sons, Inc.,, ISBN-13: 9780471739425, Pages: 499.
  37. Hore, A. and D. Ziou, 2010. Image Quality Metrics: PSNR vs. SSIM. 20th International Conference on Pattern Recognition, August 23-26, 2010, IEEE, Turkey, pp: 2366-2369.
  38. Yan, B., B. Bare, C. Ma, K. Li and W. Tan, 2019. Deep objective quality assessment driven single image super-resolution. IEEE Trans. Multimedia, 21: 2957-2971.
  39. Yang, J., Z. Lin and S. Cohen, 2013. Fast image super-resolution based on in-place example regression. IEEE Conference on Computer Vision and Pattern Recognition, June 23-28, 2013, IEEE, USA, pp: 1059-1059.
  40. Haris, M., G. Shakhnarovich and N. Ukita, 2020. Deep back-projection networks for super-resolution. IEEE Trans. Pattern Anal. Mach. Intell., 2020: 1664-1673.
  41. Li, Z., J. Yang, Z. Liu, X. Yang, G. Jeon and W. Wu, 2019. Feedback Network for Image Super-Resolution. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, IEEE, USA, pp: 3862-3871.
  42. Liu, Y., X. Zhang, S. Wang, S. Ma and W. Gao, 2020. Progressive multi-scale residual network for single image super-resolution., https://arxiv.org/abs/2007.09552.
  43. Choi, J.H., J.H. Kim, M. Cheon and J.S. Lee, 2018. Lightweight and efficient image super-resolution with block state-based recursive network., 10.48550/arXiv.1811.12546, https://arxiv.org/abs/1811.12546.
  44. Dong, C., C.C. Loy and X. Tang, 2016. Accelerating the Super-Resolution Convolutional Neural Network. In: Computer Vision – ECCV 2016, Leibe, B., J. Matas, N. Sebe and M. Welling, (Eds.)., Springer International Publishing, Cham, ISBN-27: 9783319464749,9783319464756, pp: 391-407.
  45. Niu, B., W. Wen, W. Ren, X. Zhang and L. Yang et al., 2020. Correction to: Single Image Super-Resolution via a Holistic Attention Network. In: Computer Vision – ECCV 2020., Vedaldi, A., H. Bischof, T. Brox and J.M. Frahm, (Eds.)., Springer International Publishing, Cham, ISBN-27: 9783030586096,9783030586102, pp: 191-207.
  46. Ma, C., Y. Rao, Y. Cheng, C. Chen, J. Lu and J. Zhou, 2020. Structure-preserving super resolution with gradient guidance. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 13-19, 2020, IEEE, USA, pp: 7766-7775.
  47. Gu, S., A. Lugmayr, M. Danelljan, M. Fritsche, J. Lamour and R. Timofte, 2019. DiV8K: Diverse 8K Resolution Image Dataset. IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), October 27-28, 2019, IEEE, Korea, pp: 3512-3516.
  48. Damera-Venkata, N., T.D. Kite, W.S. Geisler, B.L. Evans and A.C. Bovik, 2000. Image quality assessment based on a degradation model. IEEE Trans. Image Process., 9: 636-650.
  49. Zhang, J., Z. Wang, Y. Zheng and G. Zhang, 2020. Cascade convolutional neural network for image super-resolution., https://arxiv.org/abs/2008.10329.
  50. Robson, J.G., 1966. Spatial and temporal contrast-sensitivity functions of the visual system. J. Optical Soc. Am., 56: 1141-1142.