Deep generative adversarial residual convolutional networks for real-world super-resolution

Most current deep learning based single image super-resolution (SISR) methods focus on designing deeper / wider models to learn the non-linear mapping between low-resolution (LR) inputs and the high-resolution (HR) outputs from a large number of paired (LR/HR) training data. They usually take as assumption that the LR image is a bicubic down-sampled version of the HR image. However, such degradation process is not available in real-world settings i.e. inherent sensor noise, stochastic noise, compression artifacts, possible mismatch between image degradation process and camera device. It reduces significantly the performance of current SISR methods due to real-world image corruptions. To address these problems, we propose a deep Super-Resolution Residual Convolutional Generative Adversarial Network (SRResCGAN1) to follow the real-world degradation settings by adversarial training the model with pixel-wise supervision in the HR domain from its generated LR counterpart. The proposed network exploits the residual learning by minimizing the energy-based objective function with powerful image regularization and convex optimization techniques. We demonstrate our proposed approach in quantitative and qualitative experiments that generalize robustly to real input and it is easy to deploy for other downscaling operators and mobile/embedded devices

A Deep Residual Star Generative Adversarial Network for multi-domain Image Super-Resolution

Recently, most of state-of-the-art single image super-resolution (SISR) methods have attained impressive performance by using deep convolutional neural networks (DCNNs). The existing SR methods have limited performance due to a fixed degradation settings, i.e. usually a bicubic downscaling of low-resolution (LR) image. However, in real-world settings, the LR degradation process is unknown which can be bicubic LR, bilinear LR, nearest-neighbor LR, or real LR. Therefore, most SR methods are ineffective and inefficient in handling more than one degradation settings within a single network. To handle the multiple degradation, i.e. refers to multi-domain image super-resolution, we propose a deep Super-Resolution Residual StarGAN (SR2*GAN), a novel and scalable approach that super-resolves the LR images for the multiple LR domains using only a single model. The proposed scheme is trained in a StarGAN like network topology with a single generator and discriminator networks. We demonstrate the effectiveness of our proposed approach in quantitative and qualitative experiments compared to other state-of-the-art methods.Comment: 5 pages, 6th International Conference on Smart and Sustainable Technologies 2021. arXiv admin note: text overlap with arXiv:2009.03693, arXiv:2005.0095

Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution

Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most of the existing blind SR techniques use a degradation estimator network to explicitly estimate the blur kernel to guide the SR network with the supervision of ground truth (GT) kernels. To solve this issue, it is necessary to design an implicit estimator network that can extract discriminative blur kernel representation without relying on the supervision of ground-truth blur kernels. We design a lightweight approach for blind super-resolution (Blind-SR) that estimates the blur kernel and restores the HR image based on a deep convolutional neural network (CNN) and a deep super-resolution residual convolutional generative adversarial network. Since the blur kernel for blind image SR is unknown, following the image formation model of blind super-resolution problem, we firstly introduce a neural network-based model to estimate the blur kernel. This is achieved by (i) a Super Resolver that, from a low-resolution input, generates the corresponding SR image; and (ii) an Estimator Network generating the blur kernel from the input datum. The output of both models is used in a novel loss formulation. The proposed network is end-to-end trainable. The methodology proposed is substantiated by both quantitative and qualitative experiments. Results on benchmarks demonstrate that our computationally efficient approach (12x fewer parameters than the state-of-the-art models) performs favorably with respect to existing approaches and can be used on devices with limited computational capabilities