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

TL;DR

This paper introduces an adaptive directional gradient convolution (DGConv) that enhances real-world image super-resolution by better capturing complex gradient arrangements and textures without increasing computational costs.

Contribution

The paper proposes a novel DGConv with learnable directional gradients and an efficient fusion method, significantly improving super-resolution performance on complex textures.

Findings

DGConv improves gradient perception in super-resolution tasks.

DGPNet outperforms 15 state-of-the-art methods on multiple datasets.

The approach maintains low computational costs while enhancing detail recovery.

Abstract

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.