DenoDet V2: Phase-Amplitude Cross Denoising for SAR Object Detection

• URL: http://arxiv.org/abs/2508.09392v1
• Date: Tue, 12 Aug 2025 23:24:20 GMT
• Title: DenoDet V2: Phase-Amplitude Cross Denoising for SAR Object Detection
• Authors: Kang Ni, Minrui Zou, Yuxuan Li, Xiang Li, Kehua Guo, Ming-Ming Cheng, Yimian Dai,
• Abstract summary: We propose DenoDet V2, which exploits the complementary nature of amplitude and phase information through a band-wise mutual modulation mechanism.<n>DenoDet V2 achieves a significant 0.8% improvement on SARDet-100K dataset compared to DenoDet V1, while reducing the model complexity by half.
• Score: 49.9059941674531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the primary challenges in Synthetic Aperture Radar (SAR) object detection lies in the pervasive influence of coherent noise. As a common practice, most existing methods, whether handcrafted approaches or deep learning-based methods, employ the analysis or enhancement of object spatial-domain characteristics to achieve implicit denoising. In this paper, we propose DenoDet V2, which explores a completely novel and different perspective to deconstruct and modulate the features in the transform domain via a carefully designed attention architecture. Compared to DenoDet V1, DenoDet V2 is a major advancement that exploits the complementary nature of amplitude and phase information through a band-wise mutual modulation mechanism, which enables a reciprocal enhancement between phase and amplitude spectra. Extensive experiments on various SAR datasets demonstrate the state-of-the-art performance of DenoDet V2. Notably, DenoDet V2 achieves a significant 0.8\% improvement on SARDet-100K dataset compared to DenoDet V1, while reducing the model complexity by half. The code is available at https://github.com/GrokCV/GrokSAR.

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