Image Super-resolution Using Deep Learning
Image Super-resolution Using Deep Learning
Deep Convolutional Model is superior to perform image super-resolution because SRCNN achieves the highest PSNR (Peak Signal to Noise Ratio). It is a ratio of the maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation.The deep convolutional model in SRCNN directly learns anend-to-end mapping between low and high-resolution images with little preprocessing. This model has achieved superior performance than the state of art methods. It is also believed that more performance can be achieved by experimenting withmore filters and different strategies. Moreover, with the robustness and simplicity of the model it canalsobe used in various low-level vision problems.
PSNR is the Peak signal-to-noise ratio (PSNR) is defined as theratio ofthe maximum possible power of a signal and the power of corrupting noise that affects the fidelity of its representation.If the value of the PSNR is high the betteristhe model to reconstruct a high-resolution image from a low resolution image.
Ground-Truth Image
HR-BI (PSNR =20.497630181368823)
HR-SRCNN (PSNR=22.922696428588342)
(PSNR for HR_image and LR_image is : 20.497630181368823
PSNR for HR_image and SR_image is : 22.922696428588342
)