卷积神经网络
计算机科学
图像(数学)
分辨率(逻辑)
人工智能
计算机视觉
模式识别(心理学)
作者
Chunwei Tian,Xuanyu Zhang,Jimmy Ren,Wangmeng Zuo,Yanning Zhang,Chia-Wen Lin
出处
期刊:Cornell University - arXiv
日期:2024-02-23
标识
DOI:10.48550/arxiv.2402.15704
摘要
Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, robustness of obtained models may have challenges in varying scenes. Bigger differences of a network architecture are beneficial to extract more complementary structural information to enhance robustness of an obtained super-resolution model. In this paper, we present a heterogeneous dynamic convolutional network in image super-resolution (HDSRNet). To capture more information, HDSRNet is implemented by a heterogeneous parallel network. The upper network can facilitate more contexture information via stacked heterogeneous blocks to improve effects of image super-resolution. Each heterogeneous block is composed of a combination of a dilated, dynamic, common convolutional layers, ReLU and residual learning operation. It can not only adaptively adjust parameters, according to different inputs, but also prevent long-term dependency problem. The lower network utilizes a symmetric architecture to enhance relations of different layers to mine more structural information, which is complementary with a upper network for image super-resolution. The relevant experimental results show that the proposed HDSRNet is effective to deal with image resolving. The code of HDSRNet can be obtained at https://github.com/hellloxiaotian/HDSRNet.
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