This project implements the SRCNN (Super-Resolution Convolutional Neural Network) algorithm for single-image super-resolution. The goal of super-resolution is to generate a high-resolution image from a low-resolution input image. The SRCNN model consists of three main layers: a convolutional layer for feature extraction, a non-linear mapping layer for image reconstruction, and a deconvolutional layer for upscaling the reconstructed image. Our implementation is trained using a large dataset of pairs of low-resolution and high-resolution images, and can be used to improve the visual quality of low-resolution images. Check out my demo to see the SRCNN algorithm in action!
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This project implements SRCNN (Super-Resolution Convolutional Neural Network) for single-image super-resolution. The algorithm is trained on a dataset of low-resolution and high-resolution image pairs, and can improve the visual quality of low-resolution images by generating high-resolution images from them.
aduale04/SR_CNN
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This project implements SRCNN (Super-Resolution Convolutional Neural Network) for single-image super-resolution. The algorithm is trained on a dataset of low-resolution and high-resolution image pairs, and can improve the visual quality of low-resolution images by generating high-resolution images from them.
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