Image Denoising and Super-Resolution using Residual Learning of Deep Convolutional Network
This work addresses image quality improvement for image processing applications, but it is incremental as it replicates existing architectures.
The authors tackled the combined problem of image super-resolution and denoising by proposing a single deep learning network called SuRDCNN, which replicates existing state-of-the-art architectures and uses residual learning to handle various noise types like Gaussian and Poisson noise.
Image super-resolution and denoising are two important tasks in image processing that can lead to improvement in image quality. Image super-resolution is the task of mapping a low resolution image to a high resolution image whereas denoising is the task of learning a clean image from a noisy input. We propose and train a single deep learning network that we term as SuRDCNN (super-resolution and denoising convolutional neural network), to perform these two tasks simultaneously . Our model nearly replicates the architecture of existing state-of-the-art deep learning models for super-resolution and denoising. We use the proven strategy of residual learning, as supported by state-of-the-art networks in this domain. Our trained SuRDCNN is capable of super-resolving image in the presence of Gaussian noise, Poisson noise or any random combination of both of these noises.