FE-WRNet: Frequency-Enhanced Network for Visible Watermark Removal in Document Images

In video pipelines, document content in recorded lectures, surveillance footage, and broadcasted materials is often overlaid with persistent visible watermarks. Such overlays greatly reduce the readability of document images and interfere with downstream tasks such as optical characteristic recognition (OCR). Despite extensive studies, no prior work has concurrently addressed the diverse text layouts and watermark styles commonly encountered in real-world scenarios. To address this gap, we introduce TextLogo, the first benchmark dataset specifically designed for this comprehensive setting. TextLogo encompasses 2000 training pairs and 200 test pairs, spanning a wide array of text layouts and 30 distinct watermark styles. Building on this foundation, we propose the frequency-enhanced watermark-removal network (FE-WRNet), a generative network that fuses information from the spatial domain and the wavelet domain. Our Fused Wavelet Convolution Mixer (FWCM) effectively captures both the body and the edge components of watermarks, thereby enhancing removal performance. Training is guided by a hybrid loss function—including pixel, perceptual, and wavelet-domain objectives—to preserve fine details and edge structures. Moreover, while this work focuses on single-image document watermark removal, the proposed spatial–wavelet fusion and high-frequency-aware loss are directly relevant to video processing tasks—e.g., frame-wise watermark removal and temporal restoration—because watermarks in video often persist across frames and require fidelity-preserving, temporally-consistent restoration. Extensive experiments on TextLogo demonstrate that FE-WRNet outperforms the strongest baseline and reduces the perceptual error by 10.6%. Moreover, the proposed model also generalizes effectively to natural-image watermark datasets.