Synthetic aperture radar (SAR) image ship detection is currently a research hotspot in the field of national defense science and technology. However, SAR images contain a large amount of coherent speckle noise, which poses significant challenges in the task of ship detection. To address this issue, we propose filter convolution, a novel design that replaces the traditional convolution layer and suppresses coherent speckle noise while extracting features. Specifically, the convolution kernel of the filter convolution comes from the input and is generated by two modules: the kernel-generation module and local weight generation module. The kernel-generation module is a dynamic structure that generates dynamic convolution kernels using input image or feature information. The local weight generation module is based on the statistical characteristics of the input images or features and is used to generate local weights. The introduction of local weights allows the extracted features to contain more local characteristic information, which is conducive to ship detection in SAR images. In addition, we proved that the fusion of the proposed kernel-generation module and the local weight module can suppress coherent speckle noise in the SAR image. The experimental results show the excellent performance of our method on a large-scale SAR ship detection dataset-v1.0 (LS-SSDD-v1.0). It also achieved state-of-the-art performance on a high-resolution SAR image dataset (HRSID), which confirmed its applicability.

Low-light image enhancement (LLIE) is a fundamental but challenging task due to non-uniform illumination, severe noise, and structure degradation under poor lighting. This paper proposes GLADFormer, a novel Transformer-based framework that extends the one-stage Retinex theory by introducing perturbation terms to jointly model illumination variation and visual corruptions. The architecture consists of three components: a contrastive illumination estimator that extracts discriminative light-up features; a hierarchical corruption restorer based on window attention; and a Pixel-Aware Gated Modulation (PAGM) module for pixel-level refinement. In particular, the restorer adopts a Light-Guided Attention Block (LGAB) which leverages a window-based attention mechanism—Local Chunked Masked Attention (LCMA)—to effectively model localized spatial context while fusing global exposure cues. This design enhances the ability to recover fine details and suppress noise in complex low-light scenes. Additionally, a contrastive loss encourages robust illumination representation learning. Extensive experiments on five LLIE benchmarks and one downstream detection task demonstrate that GLADFormer achieves state-of-the-art performance with strong generalization and low computational cost.Code is available at https://github.com/JJCcxk/GLADFormer.