• Devidas Sopanrao Sonwane
• Prof. Jayanand Ambadas kamble

In this paper, Single image super-resolution (SISR) is a notoriously challenging ill-posed problem that aims to obtain a high-resolution (HR) output from one of its low-resolution (LR)versions. Recently, powerful deep learning algorithms have been applied to SISR and have achieved state-of-the-art performance. In this paper, Single image super-resolution, which is used to restore high-resolution image from a single low-resolution image, is a difficult challenging problem in computer field. In recent times, dominant deep learning algorithms have been applied to Single image super resolution and have shown an highly efficient performance. In this paper, the deep learning-based super resolution method known as a super resolution convolutional neural network (SRCNN) that takes the low-resolution image as the input and outputs the high-resolution one. SRCNN has a non-complex structure yet provides high quality and fast speed. who is carried out on different networks like Generative Adversarial Networks (GAN) and Convolutional Neural Network comparison between quality and speed.

Single Image Super Resolution (SISR), CNN, bi-cubic, Deep Convolutional Networks (DCN), ‘Set5’, ‘Set14’ and ‘Urban100’.

"A Single Image Super-Resolution (SISR) Using Deep Convolutional Networks For Improvement Of Resolution In Natural Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.b581-b587, August-2022, Available :http://www.jetir.org/papers/JETIR2208169.pdf

"A Single Image Super-Resolution (SISR) Using Deep Convolutional Networks For Improvement Of Resolution In Natural Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. ppb581-b587, August-2022, Available at : http://www.jetir.org/papers/JETIR2208169.pdf