A Heterogeneous Dynamic Convolutional Neural Network for Image Super-resolution

arXiv:2402.15704v1 Announce Type: cross
Abstract: 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 …

arxiv convolutional neural network cs.cv dynamic eess.iv image network neural network type