  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.
