Recently, deep-learning-based super-resolution methods have achieved excellent performances, but mainly focus on training a single generalized deep network by feeding numerous samples. Yet intuitively, each image has its specific representation, and is expected to acquire an adaptive model. For this issue, we propose a novel convolution modulation (CoMo) mechanism to build image-specific deep networks, by exploiting the principal information of the feature to generate a modulation weight, and thereby adaptively modulating the kernel weights of convolution without any additional parameters, which outperforms the vanilla convolution and several existing attention mechanisms when embedding into the state-of-the-art architectures. To optimize the modulated convolutions in mini-batch training, we introduce an image-specific optimization (IsO) algorithm, which tackles the infeasibility of the conventional optimization algorithms on this issue. Furthermore, we investigate the effect of CoMo on state-of-the-art architectures and design a new CoMoNet architecture by employing the U-style residual learning and hourglass dense block learning, which is an appropriate architecture to utmost improve the effectiveness of CoMo theoretically. Extensive experiments on benchmarks show that the proposed methods achieve superior performances and higher flexibility against the state-of-the-art SISR and blind SR methods. The code is available at github.com/YuanfeiHuang/CoMoNet.

Being extremely dependent on iterative estimation of the degradation prior or optimization of the model from scratch, the existing blind super-resolution (SR) methods are generally time-consuming and less effective, as the estimation of degradation proceeds from a blind initialization and lacks interpretable representation of degradations. To address it, this article proposes a transitional learning method for blind SR using an end-to-end network without any additional iterations in inference, and explores an effective representation for unknown degradation. To begin with, we analyze and demonstrate the transitionality of degradations as interpretable prior information to indirectly infer the unknown degradation model, including the widely used additive and convolutive degradations. We then propose a novel Transitional Learning method for blind Super-Resolution (TLSR), by adaptively inferring a transitional transformation function to solve the unknown degradations without any iterative operations in inference. Specifically, the end-to-end TLSR network consists of a degree of transitionality (DoT) estimation network, a homogeneous feature extraction network, and a transitional learning module. Quantitative and qualitative evaluations on blind SR tasks demonstrate that the proposed TLSR achieves superior performances and costs fewer complexities against the state-of-the-art blind SR methods. The code is available at github.com/YuanfeiHuang/TLSR.

The nonlocal self-similarity in natural image provides an effective prior for single image super-resolution (SISR), which is beneficial to contextual information capture and performance improvement, as demonstrated by conventional SISR methods. However, it is little explored to utilize this property in deep neural networks. In this paper, we propose a self-similarity prior guided (SSPG) network to incorporate self-similarity-based nonlocal operation into deep neural network for SISR. Specifically, we design a cross-scale nearest-neighbor residual (CSNNR) block via introducing cross-scale$k$-nearest neighbors (KNN) matching into a residual block, which can be flexibly integrated into deep networks to capture long-range correlations among multi-scale and multi-level features. Meanwhile, by stacking a CSNNR block and a sequence of wide-activated residual blocks with a local skip-connection, a multi-level residual self-similarity (MRSS) module is developed to effectively employ local and nonlocal information for detail recovery. Thus, through cascading multiple MRSS modules, the proposed SSPG network performs both self-similarity-based nonlocal operation and convolution-based local operation on multi-level features to reconstruct informative features for accurate SISR. In addition, for pursuing visually pleasing results, we apply our SSPG network to the perception-oriented SISR field by following the framework of generative adversarial networks. In particular, we explore a sample-discriminating learning mechanism based on the statistical descriptions of training samples, and include it in optimization procedure to automatically tune the contributions of different samples according to their characteristics and then focus the network on creating realistic results. Extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed models over the state-of-the-art methods for both distortion-oriented and perception-oriented image super-resolution tasks.

Benefiting from the strong capabilities of deep CNNs for feature representation and nonlinear mapping, deep-learning-based methods have achieved excellent performance in single image super-resolution. However, most existing SR methods depend on the high capacity of networks that are initially designed for visual recognition, and rarely consider the initial intention of super-resolution for detail fidelity. To pursue this intention, there are two challenging issues that must be solved: (1) learning appropriate operators which is adaptive to the diverse characteristics of smoothes and details; (2) improving the ability of the model to preserve low-frequency smoothes and reconstruct high-frequency details. To solve these problems, we propose a purposeful and interpretable detail-fidelity attention network to progressively process these smoothes and details in a divide-and-conquer manner, which is a novel and specific prospect of image super-resolution for the purpose of improving detail fidelity. This proposed method updates the concept of blindly designing or using deep CNNs architectures for only feature representation in local receptive fields. In particular, we propose a Hessian filtering for interpretable high-profile feature representation for detail inference, along with a dilated encoder-decoder and a distribution alignment cell to improve the inferred Hessian features in a morphological manner and statistical manner respectively. Extensive experiments demonstrate that the proposed method achieves superior performance compared to the state-of-the-art methods both quantitatively and qualitatively. The code is available at github.com/YuanfeiHuang/DeFiAN.

The performance of single image super-resolution has achieved significant improvement by utilizing deep convolutional neural networks (CNNs). The features in deep CNN contain different types of information which make different contributions to image reconstruction. However, the most CNN-based models lack discriminative ability for different types of information and deal with them equally, which results in the representational capacity of the models being limited. On the other hand, as the depth of neural network grows, the long-term information coming from preceding layers is easy to be weaken or lost at later layers, which is adverse to super-resolving image. To capture more informative features and maintain long-term information for image super-resolution, we propose a channel-wise and spatial feature modulation (CSFM) network in which a series of feature modulation memory (FMM) modules are cascaded with a densely connected structure to transform shallow features to high informative features. In each FMM module, we construct a set of channel-wise and spatial attention residual (CSAR) blocks and stack them in a chain structure to dynamically modulate the multi-level features in global and local manners. This feature modulation strategy enables the valuable information to be enhanced and the redundant information to be suppressed. Meanwhile, for long-term information persistence, a gated fusion (GF) node is attached at the end of the FMM module to adaptively fuse hierarchical features and distill more effective information via the dense skip connections and the gating mechanism. The extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over the state-of-the-art methods.

Recently, benefiting from the strong ability of feature representation, deep-learning-based methods have achieved excellent performance in single image super-resolution (SR). Furthermore, skip connection and feature fusion have been demonstrated to be a commendable strategy to deal with various features in different depth for informative reconstruction. Nevertheless, cross-layer features show diverse characteristic in detail representation, blindly fusion then introduces unavoidable interference of features. In this paper, we propose a novel feature fusion unit by utilizing alternative dilated convolutions for re-balancing diverse cross-layer features, named Feature Re-Balancing Fusion Network (RBFNet), which is theoretically and experimentally demonstrated to be robust to the interference in feature fusion for SR. Extensive experiments show that the proposed method achieves excellent performances quantitatively and qualitatively against the state-of-the-art methods.

Example learning-based image super-resolution techniques estimate a high-resolution image from a low-resolution input image by relying on high- and low-resolution image pairs. An important issue for these techniques is how to model the relationship between high- and low-resolution image patches: most existing complex models either generalize hard to diverse natural images or require a lot of time for model training, while simple models have limited representation capability. In this paper, we propose a simple, effective, robust, and fast (SERF) image super-resolver for image super-resolution. The proposed super-resolver is based on a series of linear least squares functions, namely, cascaded linear regression. It has few parameters to control the model and is thus able to robustly adapt to different image data sets and experimental settings. The linear least square functions lead to closed form solutions and therefore achieve computationally efficient implementations. To effectively decrease these gaps, we group image patches into clusters via k-means algorithm and learn a linear regressor for each cluster at each iteration. The cascaded learning process gradually decreases the gap of high-frequency detail between the estimated high-resolution image patch and the ground truth image patch and simultaneously obtains the linear regression parameters. Experimental results show that the proposed method achieves superior performance with lower time consumption than the state-of-the-art methods.

Constant power load converter will introduce unstable factor to the cascaded system. Exchanging the position of constant power load converter as the source converter can improve system stability. Active solutions to modify the negative impedance of constant power load converter can also make the system more stable. The concept of coordinative front-to-end impedance control is proposed in this paper, which can assist the cascaded system with better stability. This paper compares converter impedance behaviors between different control methods, and shows that the front-to-end impedance controller can make the system more stable. The conclusions have been validated by simulation results.