1D kernel distillation network for efficient image super-resolution

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Yusong Li, Longwei Xu, Weibin Yang, Dehua Geng, Mingyuan Xu, Zhiqi Dong, Pengwei Wang
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引用次数: 0

Abstract

Recently, there have been significant strides in single-image super-resolution, especially with the integration of transformers. However, the escalating computational demands of large models pose challenges for deployment on edge devices. Therefore, in pursuit of Efficient Image Super-Resolution (EISR), achieving a better balance between task computational complexity and image fidelity becomes imperative. In this paper, we introduce the 1D kernel distillation network (OKDN). Within this network, we have devised a lightweight 1D Large Kernel (OLK) block, incorporating a more lightweight yet highly effective attention mechanism. This block significantly expands the effective receptive field, enhancing performance while mitigating computational costs. Additionally, we develop a Channel Shift Enhanced Distillation (CSED) block to improve distillation efficiency, allocating more computational resources towards increasing network depth. We utilize methods involving partial channel shifting and global feature supervision (GFS) to further augment the effective receptive field. Furthermore, we introduce learnable Gaussian perturbation convolution (LGPConv) to enhance the model’s feature extraction and performance capabilities while upholding low computational complexity. Experimental results demonstrate that our proposed approach achieves superior results with significantly lower computational complexity compared to state-of-the-art models. The code is available at https://github.com/satvio/OKDN.

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来源期刊
Image and Vision Computing
Image and Vision Computing 工程技术-工程:电子与电气
CiteScore
8.50
自引率
8.50%
发文量
143
审稿时长
7.8 months
期刊介绍: Image and Vision Computing has as a primary aim the provision of an effective medium of interchange for the results of high quality theoretical and applied research fundamental to all aspects of image interpretation and computer vision. The journal publishes work that proposes new image interpretation and computer vision methodology or addresses the application of such methods to real world scenes. It seeks to strengthen a deeper understanding in the discipline by encouraging the quantitative comparison and performance evaluation of the proposed methodology. The coverage includes: image interpretation, scene modelling, object recognition and tracking, shape analysis, monitoring and surveillance, active vision and robotic systems, SLAM, biologically-inspired computer vision, motion analysis, stereo vision, document image understanding, character and handwritten text recognition, face and gesture recognition, biometrics, vision-based human-computer interaction, human activity and behavior understanding, data fusion from multiple sensor inputs, image databases.
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