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| Image Classification Network with Frequency Decay Convolution |
| YUAN Heng1, ZHUO Jikun1, ZHANG Shengchong2 |
1. College of Software, Liaoning Technical University, Huludao 125105;
2. Key Laboratory of Optoelectronic Information Control and Security Technology, Tianjin 300308 |
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Abstract In traditional convolutional neural networks,the lack of explicit constraints on the frequency-domain responses of convolutional kernels leads to excessive enhancement of high-frequency components. To address this issue, an image classification network with frequency decay convolution(FDNet) is proposed. First, the frequency decay convolution(FDConv) module is designed. The neuron signal attenuation mechanism is introduced into the frequency domain. By attenuating the frequency-domain amplitude of the convolutional kernel, the responses of excessively strong frequency components are suppressed with complete phase information retained. Consequently, the overfitting problem caused by imbalance in frequency-domain responses is effectively alleviated. Second, FDConv is embedded into the shallow feature extraction stage and the residual block structure, enabling spectral regularization throughout the entire feature extraction process. Then, the directional spatial decay(DSD) module is constructed. Parallel 1×3 and 3×1 FDConv modules are adopted to separately extract horizontal and vertical directional features, thereby achieving collaborative enhancement of directional features and important channels. Finally, the DSD module is embedded at the end of the residual block. After the residual connection, both directional feature decomposition and channel recalibration are performed, enabling the network to focus on key discriminative information. Experiments on CIFAR-10, CIFAR-100, SVHN, STL-10, Imagenette, and Imagewoof image datasets demonstrate the superior classification performance of FDNet.
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Received: 31 March 2026
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| Fund:National Natural Science Foundation of China(No.61601213), Natural Science Foundation of Liaoning Province(No.20170540426), Key Project of Educational Department of Liaoning Province(No.LJYL049) |
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Corresponding Authors:
YUAN Heng, Ph.D., associate professor. Her research interests include image processing, pattern recognition and artificial intelligence.
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About author:: ZHUO Jikun, Master student. Her research interests include image processing, pattern recognition and artificial intelligence.
ZHANG Shengchong, Master, senior engineer. His research interests include digital signal processing, pattern recognition and artificial intelligence. |
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2026, Vol. 39 
