Deep Learning for Single Image Super-Resolution: A Brief Review

Single image super-resolution (SISR) is a notoriously challenging ill-posed problem, which aims to obtain a high-resolution (HR) output from one of its low-resolution (LR) versions. To solve the SISR problem, recently powerful deep learning algorithms have been employed and achieved the state-of-the-art performance. In this survey, we review representative deep learning-based SISR methods, and group them into two categories according to their major contributions to two essential aspects of SISR: the exploration of efficient neural network architectures for SISR, and the development of effective optimization objectives for deep SISR learning. For each category, a baseline is firstly established and several critical limitations of the baseline are summarized. Then representative works on overcoming these limitations are presented based on their original contents as well as our critical understandings and analyses, and relevant comparisons are conducted from a variety of perspectives. Finally we conclude this review with some vital current challenges and future trends in SISR leveraging deep learning algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM

Real-World Image Restoration Using Degradation Adaptive Transformer-Based Adversarial Network

Most existing learning-based image restoration methods heavily rely on paired degraded/non-degraded training datasets that are based on simplistic handcrafted degradation assumptions. These assumptions often involve a limited set of degradations, such as Gaussian blurs, noises, and bicubic downsampling. However, when these methods are applied to real-world images, there is a significant decrease in performance due to the discrepancy between synthetic and realistic degradation. Additionally, they lack the flexibility to adapt to unknown degradations in practical scenarios, which limits their generalizability to complex and unconstrained scenes. To address the absence of image pairs, recent studies have proposed Generative Adversarial Network (GAN)-based unpaired methods. Nevertheless, unpaired learning models based on convolution operations encounter challenges in capturing long-range pixel dependencies in real-world images. This limitation stems from their reliance on convolution operations, which offer local connectivity and translation equivariance but struggle to capture global dependencies due to their limited receptive field. To address these challenges, this dissertation proposed an innovative unpaired image restoration basic model along with an advanced model. The proposed basic model is the DA-CycleGAN model, which is based on the CycleGAN [1] neural network and specifically designed for blind real-world Single Image Super-Resolution (SISR). The DA-CycleGAN incorporates a degradation adaptive (DA) module to learn various real-world degradations (such as noise and blur patterns) in an unpaired manner, enabling strong flexible adaptation. Additionally, an advanced model called Trans-CycleGAN was designed, which integrated the Transformer architecture into CycleGAN to leverage its global connectivity. This combination allowed for image-to-image translation using CycleGAN [1] while enabling the Transformer to model global connectivity across long-range pixels. Extensive experiments conducted on realistic images demonstrate the superior performance of the proposed method in solving real-world image restoration problems, resulting in clearer and finer details. Overall, this dissertation presents a novel unpaired image restoration basic model and an advanced model that effectively address the limitations of existing approaches. The proposed approach achieves significant advancements in handling real-world degradations and modeling long-range pixel dependencies, thereby offering substantial improvements in image restoration tasks. Index Terms— Cross-domain translation, generative adversarial network, image restoration, super-resolution, transformer, unpaired training

Variational Deep Image Restoration

This paper presents a new variational inference framework for image restoration and a convolutional neural network (CNN) structure that can solve the restoration problems described by the proposed framework. Earlier CNN-based image restoration methods primarily focused on network architecture design or training strategy with non-blind scenarios where the degradation models are known or assumed. For a step closer to real-world applications, CNNs are also blindly trained with the whole dataset, including diverse degradations. However, the conditional distribution of a high-quality image given a diversely degraded one is too complicated to be learned by a single CNN. Therefore, there have also been some methods that provide additional prior information to train a CNN. Unlike previous approaches, we focus more on the objective of restoration based on the Bayesian perspective and how to reformulate the objective. Specifically, our method relaxes the original posterior inference problem to better manageable sub-problems and thus behaves like a divide-and-conquer scheme. As a result, the proposed framework boosts the performance of several restoration problems compared to the previous ones. Specifically, our method delivers state-of-the-art performance on Gaussian denoising, real-world noise reduction, blind image super-resolution, and JPEG compression artifacts reduction.Comment: IEEE Transactions on Image Processing (TIP 2022

Generalized Expectation Maximization Framework for Blind Image Super Resolution

Learning-based methods for blind single image super resolution (SISR) conduct the restoration by a learned mapping between high-resolution (HR) images and their low-resolution (LR) counterparts degraded with arbitrary blur kernels. However, these methods mostly require an independent step to estimate the blur kernel, leading to error accumulation between steps. We propose an end-to-end learning framework for the blind SISR problem, which enables image restoration within a unified Bayesian framework with either full- or semi-supervision. The proposed method, namely SREMN, integrates learning techniques into the generalized expectation-maximization (GEM) algorithm and infers HR images from the maximum likelihood estimation (MLE). Extensive experiments show the superiority of the proposed method with comparison to existing work and novelty in semi-supervised learning