• D. Venupriya
• K. Greeshmasri
• B. Satyapriyanka
• CH. Maachiraju
• T. Sreenivasu

Compared to low resolution (LR) photos, high resolution (HR) photographs include more information about the image[1]. The HR picture may be retrieved from many low resolution images more readily than it can from a single LR image. To create an HR picture, many LR photos that contain different features can be combined. Reproducing an HR image with a single, less subtle LR image is still extremely difficult. In order to improve resolution, a neural network-based model based on feature extraction from a single picture is suggested in this research. Each LR and HR picture is broken up into smaller blocks, and a feature is computed for each block in the histogram[4]. The histogram properties of LR are learned using back propagation neural network (BPNN). The simulation was performed on a set of brain MRI images, and the findings show that the resultant neural network model has improved the PSNR and RSME.

Histogram, resolution, digital image processing, and neural network

"HISTOGRAM BASED RESOLUTION OF AN IMAGE BY USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 4, page no.k542-k547, April-2023, Available :http://www.jetir.org/papers/JETIR2304A76.pdf

"HISTOGRAM BASED RESOLUTION OF AN IMAGE BY USING CONVOLUTIONAL NEURAL NETWORK", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 4, page no. ppk542-k547, April-2023, Available at : http://www.jetir.org/papers/JETIR2304A76.pdf