• Shubham Dwivedi
• Dr. Babita Pathik,

In The super resolution (SR), the method of obtaining an image with high resolution (HR) through processing a low-resolution (LR) picture, being the primary concern of the present research. During single image super resolution (SISR), the improved very deep super resolution (IVDSR) was used to deliver an improved version of the SISR. The suggested approach depends on CNN, for instance, with a network depth of 20 and several image attributes to train that include apply up-sampling as well as residual images—a vital phase in SISR. The bi-cubic approach and the suggested deep learning based on Modified CNN with rectified linear Unit 6 —are combined to enhance the methodology that is being described. The methodology that will be considering determines improved PSNR and SSIM outcomes. Both of these factors are crucial for the final results analysis of the image super resolution (ISR) research. Testing data sets, such as the UC Mecred land collection, are available for the training and testing of the approach given. When compared to other prior techniques, the suggested technique produces superior results. According on the WHU-RS19 dataset set, the suggested method's output will be compared to that of SRCNN, VDSR, D-DBPN, RCAN, SRFBN, SAN, Hybrid Method [1], and the proposed MSISR. Use MATLAB 2020A for the suggested method's simulation.

IndexTerms - Super Resolution, Deep Neural Network, Image Super Resolution, Convolution Neural Network, Remote Sensing Image, Up Sampling, Residual.

"Synthetic Aperture Radar Image Super Resolution Using Deep Learning Based on Modified CNN With Rectified Linear Unit6 (RELU6)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 2, page no.e423-e431, February-2024, Available :http://www.jetir.org/papers/JETIR2402462.pdf

"Synthetic Aperture Radar Image Super Resolution Using Deep Learning Based on Modified CNN With Rectified Linear Unit6 (RELU6)", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 2, page no. ppe423-e431, February-2024, Available at : http://www.jetir.org/papers/JETIR2402462.pdf