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 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.

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 paper 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 infrared (IR) image dynamic range compression (DRC) technology involves compressing high dynamic range (HDR) IR images into low dynamic range (LDR) images for display on common devices. To facilitate human observation, DRC methods should preserve the structural information as much as possible while adjusting the contrast of HDR IR images. However, existing DRC methods struggle to adapt to various highly dynamic IR scenes when using fixed parameter settings. To address this limitation, a novel gradient domainbased DRC method with Adaptive Contrast Adjustment and Structure Preservation (ACASP) is proposed. Our ACASP adapts local contrast and gradients by analyzing local features of HDR IR images, effectively handling different HDR IR scenes. We introduce local contrast and variance to enhance visibility in low-contrast areas and preserve details in high-contrast areas. Specifically, we design a contrast-adaptive mapping curve and a gradient-adaptive modulation factor to optimize both contrast and structure in the LDR image. Extensive experiments on three public HDR IR datasets demonstrate that the proposed method can outperform state-of-the-art DRC methods in both quantitative and qualitative analyses. This work contributes to the field by offering a more adaptive and robust approach to IR image DRC.