A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution
<|reference_start|>A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution: 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.<|reference_end|>
@article{tian2024adaptive,
    title={Adaptive Convolutional Neural Network for Image Super-resolution},
    author={Chunwei Tian, Xuanyu Zhang, Tao Wang, Yongjun Zhang, Qi Zhu, Chia-Wen
  Lin},
    journal={arXiv preprint arXiv:2402.15704},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.15704},
    primaryClass={eess.IV cs.CV}
}