DenoDet: Attention as Deformable Multisubspace Feature Denoising for Target Detection in SAR Images

Synthetic aperture radar (SAR) target detection has long been impeded by inherent speckle noise and the prevalence of diminutive ambiguous targets. While deep neural networks have advanced SAR target detection, their intrinsic low-frequency bias and static post-training weights falter with coherent noise and preserving subtle details across heterogeneous terrains. Motivated by traditional SAR image denoising, we propose DenoDet, a network aided by explicit frequency-domain transform to calibrate convolutional biases and pay more attention to high frequencies, forming a natural multiscale subspace representation to detect targets from the perspective of multisubspace denoising. We design TransDeno, a dynamic frequency-domain attention module that performs as a transform-domain soft thresholding operation, dynamically denoising across subspaces by preserving salient target signals and attenuating noise. To adaptively adjust the granularity of subspace processing, we also propose a deformable group fully connected layer that dynamically varies the group conditioned on the input features. Without bells and whistles, our plug-and-play TransDeno sets State-of-the-Art scores on multiple SAR target detection datasets.