Abstract：To improve the modeling capability of image classification networks for critical information and enhance the completeness and discriminability of feature representation, a dual wavelet-enhanced network for image classification(DWENet) is proposed. First, a wavelet-gated convolution module is constructed during the shallow feature extraction stage of the backbone network. By integrating wavelet frequency domain decomposition and a gating mechanism, edge and texture information are effectively captured with key details preserved and redundant noise suppressed. Second, a wavelet kernel attention module is introduced after the max-pooling layer. Frequency awareness and large receptive field spatial modeling are integrated to enhance structural representation and long-range dependency perception. As a result, the information loss caused by pooling operations is compensated. Furthermore, a dual-path feature enhancement module(DFEM) is incorporated into the deep network layers. DFEM enhances mid-and high-level semantic representation and key region responsiveness by reorganizing features through spatial-frequency decomposition and incorporating channel attention mechanisms. While maintaining computational efficiency, DWENet effectively mitigates core issues such as shallow feature decay and limited spatial perception. Experiments on CIFAR-10, CIFAR-100, SVHN, Imagenette, and Imagewoof datasets demonstrate that DWENet improves classification accuracy.
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