Wavelet Boost Identification-based Multi-Level Refinement Network For Camouflaged Object Detection

Camouflaged Object Detection (COD) aims to segment objects that are visually integrated into their surroundings. Despite remarkable progress, existing methods still struggle with the dual challenges of omitting detail discrimination and encountering feature redundancy, thereby failing to achieve optimal performance. Addressing these problems, we propose a novel network architecture: Wavelet Boost Identification-based Multi-level Refinement Network (WBRNet) for COD. WBRNet employs Wavelet Discrimination Boost to enhance feature recognition capabilities and effectively suppress noise. It applies distinct feature extraction strategies to high-frequency and low-frequency regions, focusing particularly on detail-rich high-frequency areas to overcome detail discrimination omission. WBRNet then integrates the Mamba framework with asymmetric convolutions, providing a more extensive receptive field. Finally, utilizing our proposed Feature Reversal Decoder, WBRNet precisely directs attention to specific image patches and generates counter-masks, enhancing the accuracy of feature extraction. Comparative results across multiple benchmark datasets demonstrate that the WBRNet outperforms 13 state-of-the-art (SOTA) methods with remarkable outcomes.