Feature-refined adaptive modulation transformer for image deraining

Abstract

Recent image deraining methods demonstrate impressive reconstruction performance by leveraging the global modeling capability of Transformer architecture. However, unlike convolutional approach, Transformer inherently struggles to capture high-frequency detail effectively. Furthermore, existing methods primarily focus on spatial information while largely neglecting the frequency-domain characteristics of rain streaks, which are crucial for rain removal. To address these challenges, we propose a feature-refined adaptive modulation Transformer (FRAMT), which effectively integrates spatial-domain features with frequency-domain modulation to enhance deraining performance. To accurately identify rain streaks and efficiently separate them from the background, the detail-guided attention block enhances sensitivity to high-frequency components by integrating pooling operation with convolution. To mitigate image blurring and detail loss induced by rain streaks, the local feature refinement block employs a multi-scale content decomposition strategy, utilizing a parallel multi-branch architecture to extract diverse contextual features across varying spatial scales. Additionally, the adaptive fusion modulation block incorporates a frequency selection mechanism that dynamically modulates feature response, effectively suppressing redundant information and irrelevant features. Extensive experiments conducted on widely used benchmark datasets demonstrate that the proposed method is more competitive than advanced methods.