2024/3/15 -We propose a deep learning method for single image super-resolution (SR). Our method directly learns an end-to-end mapping be-tween the low/high-resolution ...
2024/3/15 -In this paper, we aim at accelerating the current SRCNN, and propose a compact hourglass-shape CNN structure for faster and better SR.
2024/3/26 -SRCNN developed as the frontrunner, showing the most elevated PSNR of 28.7 and a commendable SSIM of 0.89. FSRCNN and ESPCN were closely taken after with PSNR ...
2024/5/15 -Output of the super-resolution convolutional neural network (SRCNN) using a different training set with "Jilin-1" satellite video (left) and Yang91 (right).
feel free to ask me any question ================== Code https://github.com/AhmedIbrahimai/Super-Resolution-image-using-CNN-in-PyTorch ...
YouTube-Ahmed ibrahim
2024/7/28 -SRCNN was a game-changer. It showed that neural networks could indeed improve image quality beyond what bicubic interpolation could offer. SRCNN ...
2024/4/1 -In this study, we developed a model that can dramatically increase the spatial resolution of medical images using deep learning technology.
2024/8/25 -This paper provides a comprehensive survey on deep-learning-based super-resolution methods along with their applications and limitations.
2024/4/11 -SRCNN maps low-resolution images to high-resolution output directly. It's effective for real-time upscaling tasks. You can learn more about SRCNN here.
2024/3/26 -Super Resolution Convolution Neural Network (SRCNN) is one of the first methods to use deep learning for image super-resolution. SRCNN is a convolutional neural ...