Adaptive Convolutional Neural Network for Image Super-resolution

TL;DR

This paper introduces ADSRNet, an adaptive CNN architecture with heterogeneous parallel networks designed to improve image super-resolution across diverse scenes by capturing more structural information and enhancing model robustness.

Contribution

The paper proposes a novel adaptive CNN with a dual-network structure that enhances robustness and structural information extraction for image super-resolution.

Findings

ADSRNet outperforms existing methods in super-resolution tasks.

The heterogeneous parallel architecture improves adaptability to different scenes.

Experimental results validate the effectiveness of ADSRNet.

Abstract

Convolutional neural networks can automatically learn features via deep network architectures and given input samples. However, the robustness of obtained models may face challenges in varying scenes. Bigger differences in network architecture are beneficial to extract more diversified structural information to strengthen the robustness of an obtained super-resolution model. In this paper, we proposed a adaptive convolutional neural network for image super-resolution (ADSRNet). To capture more information, ADSRNet is implemented by a heterogeneous parallel network. The upper network can enhance relation of context information, salient information relation of a kernel mapping and relations of shallow and deep layers to improve performance of image super-resolution. That can strengthen adaptability of an obtained super-resolution model for different scenes. 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 ADSRNet is effective to deal with image resolving. Codes are obtained at https://github.com/hellloxiaotian/ADSRNet.