Real-SR endeavors to produce high-resolution images with rich details while
mitigating the impact of multiple degradation factors. Although existing
methods have achieved impressive achievements in detail recovery, they still
fall short when addressing regions with complex gradient arrangements due to
the intensity-based linear weighting feature extraction manner. Moreover, the
stochastic artifacts introduced by degradation cues during the imaging process
in real LR increase the disorder of the overall image details, further
complicating the perception of intrinsic gradient arrangement. To address these
challenges, we innovatively introduce kernel-wise differential operations
within the convolutional kernel and develop several learnable directional
gradient convolutions. These convolutions are integrated in parallel with a
novel linear weighting mechanism to form an Adaptive Directional Gradient
Convolution (DGConv), which adaptively weights and fuses the basic directional
gradients to improve the gradient arrangement perception capability for both
regular and irregular textures. Coupled with DGConv, we further devise a novel
equivalent parameter fusion method for DGConv that maintains its rich
representational capabilities while keeping computational costs consistent with
a single Vanilla Convolution (VConv), enabling DGConv to improve the
performance of existing super-resolution networks without incurring additional
computational expenses. To better leverage the superiority of DGConv, we
further develop an Adaptive Information Interaction Block (AIIBlock) to adeptly
balance the enhancement of texture and contrast while meticulously
investigating the interdependencies, culminating in the creation of a DGPNet
for Real-SR through simple stacking. Comparative results with 15 SOTA methods
across three public datasets underscore the effectiveness and efficiency of our
proposed approach.

通过引入卷积核内核差分操作和可学习的方向梯度卷积，以及使用自适应的信息交互块，提出了一种适用于真实超分辨率的 DGPNet 方法，通过简单堆叠来平衡纹理增强和对比度，并通过与其他方法的比较结果证明了该方法的有效性和高效性。

自适应方向梯度卷积实现高效真实世界图像超分辨率

Efficient Real-world Image Super-Resolution Via Adaptive Directional  Gradient Convolution

In light of the significant progress made in the development and application
of semantic segmentation tasks, there has been increasing attention towards
improving the robustness of segmentation models against natural degradation
factors (e.g., rain streaks) or artificially attack factors (e.g., adversarial
attack). Whereas, most existing methods are designed to address a single
degradation factor and are tailored to specific application scenarios. In this
work, we present the first attempt to improve the robustness of semantic
segmentation tasks by simultaneously handling different types of degradation
factors. Specifically, we introduce the Preprocessing Enhanced Adversarial
Robust Learning (PEARL) framework based on the analysis of our proposed Naive
Adversarial Training (NAT) framework. Our approach effectively handles both
rain streaks and adversarial perturbation by transferring the robustness of the
segmentation model to the image derain model. Furthermore, as opposed to the
commonly used Negative Adversarial Attack (NAA), we design the Auxiliary Mirror
Attack (AMA) to introduce positive information prior to the training of the
PEARL framework, which improves defense capability and segmentation
performance. Our extensive experiments and ablation studies based on different
derain methods and segmentation models have demonstrated the significant
performance improvement of PEARL with AMA in defense against various
adversarial attacks and rain streaks while maintaining high generalization
performance across different datasets.

通过引入基于 Naive Adversarial Training 框架的 Preprocessing Enhanced Adversarial Robust Learning (PEARL) 框架，同时处理不同类型的退化因素，如雨刷和对抗性攻击，并进一步设计了一种辅助镜像攻击方法 (AMA) 来提高防御能力和分割性能。