Full Length Research Paper
References
|
Ahmad T, Qureshi SS (2012). Grid analysis of MISO image super resolution techniques. Advanced Communication Technology (ICACT). pp. 1242-1246. |
|
|
Aru OE, Achumba IE, Opara FK (2016). Exploration of the Adaptive Neuro–Fuzzy Inference System Architecture and its Applications. Am. J. Eng. Res. 5(9):181-188. |
|
|
Bajić B, Lindblad J, Sladoje N (2016). Single image super-resolution reconstruction in presence of mixed Poisson-Gaussian noise. Image Processing Theory Tools and Applications (IPTA) pp. 1-6. |
|
|
Boskovitz V, Guterman H (2002). An adaptive neuro-fuzzy system for automatic image segmentation and edge detection. IEEE Trans. Fuzzy Syst. 10(2): 247-262. |
|
|
Caramihale T, Popescu D, Ichim L (2016). Detection of regions of interest in retinal images using artificial neural networks and K-means clustering. In: Applied Electromagnetics and Communications (ICECOM), 2016 22nd International Conference on IEEE. pp. 1-6. |
|
|
Faramarzi E, Rajan D, Christensen MP (2013). Unified Blind Method for Multi-Image Super-Resolution and Single/Multi-Image Blur Deconvolution. IEEE Trans Image Process. 22(6):2101-2114. |
|
|
Farsiu S, Robinson MD, Elad M, Milanfar P (2004). Fast and robust multiframe super-resolution. IEEE Trans. Image Process. 13:1327-1344. |
|
|
Freeman WT, Jones TR, Pasztor EC (2002). Example based super resolution. IEEE Comput. Graphics Appl. 22(2):56-65. |
|
|
Gohshi S (2016). Frequency domain analysis of Super Resolution Image Reconstruction and super resolution with nonlinear processing. World Automation Congress (WAC). pp. 1-6. |
|
|
Grover R, Kasana SS (2015). Image super resolution by fast edge-adaptive interpolation. Adv. Comput. Commun. Syst. pp. 1-5. |
|
|
Hou HS, Andrews HC (1978). Cubic splines for image interpolation and digital filtering. IEEE Trans. Signal Process. 26(6):508-517. |
|
|
Huynh-Thu Q, Ghanbari M (2008). Scope of validity of PSNR in image/video quality assessment. Electr. Lett. 44(13):800-801. |
|
|
Kim KI, Kwon Y (2008). Example-Based Learning for Single-Image Super-Resolution. 30th DAGM. pp. 456-465. |
|
|
Kim SP, Bose NK, Valenzuela HM (1990). Recursive reconstruction of high resolution image from noisy under sampled multiframes. IEEE Trans. Accost. Speech Signal Process. 38(6):1013-1027. |
|
|
Kim SP, Su WY (1993). Recursive high-resolution reconstruction of blurred multiframe images. IEEE Trans. Image Process. 2(4):534-539. |
|
|
Komatsu T, Aizawa K, Igarashi T, Saito T (1993). Signal-processing based method for acquiring very high resolution image with multiple cameras and its theoretical analysis. Proc. Inst. Elect. Eng. 140(1):19-25. |
|
|
Lazli L, Boukadoum M (2017). Image enhancement segmentation Indonesian's Batik based on fuzzy C-means clustering using median filter. Application for Technology of Information and Communication (iSemantic). pp. 112-118. |
|
|
Li M, Nguyen T (2008). Markov random field model-based edge-directed image interpolation. IEEE Trans. Image Process. 17(7):1121-1128. |
|
|
Li X, Orchard MT (2001). New edge-directed interpolation. IEEE Trans. Image Process. 10(10):1521-1527. |
|
|
Lu X, Yan P, Yuan Y, Li X, Yuan H (2011). Utilizing homotopy for single image superresolution. Pattern Recognition (ACPR). pp. 316-320. |
|
|
Montgomery DC, Runger GC (2010). Applied Statistics and Probability for Engineers (5nd Edition SI Version.), Wiley, United States. |
|
|
Navas KA, Gayathri DKG, Athulya MS, Vasudev A (2011). MWPSNR: A new image fidelity metric. Recent Adv. Intell. Comput. Syst. pp. 627-632. |
|
|
Ngernplubpla J, Chitsobhuk O (2013). Image Super Resolution with Adaptive Edge Enhancement Algorithm. Fifth International Conference on Graphic and Image Processing (ICGIP 2013). |
|
|
Ngernplubpla J, Chitsobhuk O (2015). Image enhancement based on edge boosting algorithm. Seventh International Conference on Graphic and Image Processing (ICGIP 2015). |
|
|
Ngocho BM, Mwangi E (2016). Single image super resolution with guided back-projection and LoG sharpening. Electrotechnical Conference Mediterranean. pp. 1-6. |
|
|
Osowski S, Linh TH, Brudzewski K (2002). Neuro-fuzzy network for flavor recognition and classification. Instrumentation and Measurement Technology Conference (IMTC). pp. 1597-1601. |
|
|
Panella M, Gallo AS (2005). An input-output clustering approach to the synthesis of ANFIS networks. IEEE Trans. Fuzzy Syst. 13(1):69-81. |
|
|
Rigau J, Feixas M, Sbert S (2004). An information theoretic framework for image segmentation. International Conference on Image Processing, (ICIP). pp. 1193-1196. |
|
|
Roohi F, Phil M (2013). Neuro Fuzzy Approach to Data Clustering: Framework for Analysis. Eur. Sci. J. 9(9). |
|
|
Shi W, Caballero J, Huszár F, Totz J, Aitken AP, Bishop R, Rueckert D, Wang Z (2016). Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network. Computer Vision and Pattern Recognition (CVPR). pp. 1874-1883. |
|
|
Suo S, He X, Chen H, Xiong S, Teng Q (2017). Cubic splines for image interpolation and digital filtering. Image Vision and Computing (ICIVC). pp. 473-477. |
|
|
Tai YW, Liu S, Brown MS, Lin S (2010). Super resolution using edge prior and single image detail synthesis. IEEE Conference on Computer Vision and Pattern Recognition. pp. 2400-2407. |
|
|
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004). Image quality assessment: From error visibility to structural similarity. IEEE Trans. Image Process. 13(4):600-612. |
|
|
Yan Q, Xu Y, Yang X, Nguyen TQ (2015). Single Image Superresolution Based on Gradient Profile Sharpness. IEEE Trans. Image Process. 24(10):3187-3202. |
|
|
Yang J, Wright J, Huang T, Ma Y (2010). Image super-resolution via sparse representation. IEEE. Trans. Image Process. 19(11):2861-2873. |
|
|
Yeung DS, Wang XZ (2000). Using a neuro-fuzzy technique to improve the clustering based on similarity. Systems Man and Cybernetics. pp. 3693-3698. |
|
|
Zhang K, Gao X, Tao D, Li X (2012). Single Image Super-Resolution With Non-Local Means and Steering Kernel Regression. IEEE Trans. Image Process. 21:4544-4556. |
|
|
Zhang L, Wu X (2006). An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans. Image Process. 15(8):2226-2238. |
|
Copyright © 2026 Author(s) retain the copyright of this article.
This article is published under the terms of the Creative Commons Attribution License 4.0
