References
[1]. Das, A., Viji, K. S. A., & Sebastian, L. (2021). A survey on
image and video super resolution with deep learning
models. International Journal of Engineering Research &
Technology, 9(13), 30-35.
[2]. Gupta, V., & Mehra, S. (2016). Image fusion
techniques-a comparative study. International Journal of
Engineering Trends and Technology, 32(2), 113-118.
[4]. Kumar, M. P., & Kumar, P. R. (2018). Image Fusion
based edge detection using mathematical morphology.
International Journal of Engineering Research &
Technology, 1(3), 108-111.
[5]. Mishra, D., & Palkar, B. (2015). Image fusion
techniques: A review. International Journal of Computer
Applications, 130(9), 7-13.
[6]. Mohamed, N. A., & Al-Tamimi, M. S. H. (2020). Image
fusion using a convolutional neural network. Solid State
Technology, 63(6), 13149-13162.
[7]. Nahas, R. (2020). Enhancement process for grayscale
images resulted from image fusion. International
Journal of Engineering Research & Technology, 9(9), 150-155.
[8].
Pandurangan, D., Kumar, R. S., Gebremariam, L.,
Arulmurugan, L., & Tamilselvan, S. (2021). Combined
gray level transformation technique for low light color
image enhancement. Journal of Computational and
Theoretical Nanoscience, 18(4), 1221-1226.
[9]. Pawal, T. B., & Patil, S. N. (2020). MRI, CT and PET image
fusion using 3-D discrete shearlet transform and global to
local fusion rule. International Journal of Engineering
Science and Computing, 10(9), 27370–27373.
[11]. Rani, K., & Sharma, R. (2013). Study of different
image fusion algorithm. International Journal of
Emerging Technology and Advanced Engineering, 3(5),
288-291.
[12]. Saranya, C., & Shoba, S. (2015). Efficient image
fusion technique by transform based methods.
International Journal of Applied Engineering Research,
10(20), 16321-16324.
[13]. Shirkande, S., Jain, J., & Lengare, M. (2020).
Enhancing under water images using fusion of
wavelength compensation image dehazing and wavelet
transform. Solid State Technology, 63(6), 3720-3730.
[14]. Singh, M. K. (2020). Review paper on CNN based
computer vision. International Research Journal of
Modernization in Engineering Technology and Science,
2(8), 1367-1371.
[16]. Sujitha, S. M. S., Kannan, P., Sreeja, P. G., &
Maheswari, S. (2021). Adaptive hybrid remote sensing
image fusion of panchromatic and multispectral images.
Solid State Technology, 64(2), 996-1011.
[17].
Tian, L., Cao, Y., He, B., Zhang, Y., He, C., & Li, D.
(2021). Image enhancement driven by object
characteristics and dense feature reuse network for ship
target detection in remote sensing imagery. Remote
Sensing, 13(7), 1327.
[18]. Vijay, I., Banwari, H., Saluja, G., & Khatri, A. (2021).
Improving speech enhancement using generative
adversarial networks (segan) by using multistageenhancement.
International Research Journal of
Modernization in Engineering Technology and Science,
3(6), 214–217.
[19].
Wang, T. C., Liu, M. Y., Zhu, J. Y., Tao, A., Kautz, J., &
Catanzaro, B. (2018). High-resolution image synthesis
and semantic manipulation with conditional gans. In
Proceedings of the IEEE Conference on Computer Vision
and Pattern Recognition (pp. 8798-8807).
[20].
Xia, G. S., Bai, X., Ding, J., Zhu, Z., Belongie, S., Luo,
J., ... & Zhang, L. (2018). DOTA: A large-scale dataset for
object detection in aerial images. In Proceedings of the
IEEE Conference on Computer Vision and Pattern
Recognition (pp. 3974-3983).