Watermark segmentation

Summary

Watermark Segmentation This repository by Diffusion Dynamics, showcases the core technology behind the watermark segmentation capabilities of our first product, clear.photo. This work leverages insights from research on diffusion models for image restoration tasks. Introduction Effective watermark removal hinges on accurately identifying the watermark's precise location and shape within the image. This code tackles the first crucial step: watermark segmentation. We present a deep learning approach trained to generate masks highlighting watermark regions. This repository focuses on segmenting logo-based watermarks, demonstrating a robust technique adaptable to various watermark types. The methodologies employed draw inspiration from advancements in image segmentation. This repository aims to consolidate key ideas from recent research in visible watermark removal and segmentation, including techniques presented in: arXiv:2108.03581 "Visible Watermark Removal via Self-calibrated Localization and Background Refinement" arXiv:2012.07616 "WDNet: Watermark-Decomposition Network for Visible Watermark Removal" arXiv:2312.14383 "Removing Interference and Recovering Content Imaginatively for Visible Watermark Removal" arXiv:2502.02676 "Blind Visible Watermark Removal with Morphological Dilation" It distills these concepts into a minimal, functional codebase focused purely on the segmentation task. The goal is to provide a clear, understandable baseline that is easy to modify and build upon, even allowing for fine-tuning on consumer hardware like laptops with Apple M-series chips. It serves as a foundational example demonstrating the core techniques applicable to building more complex tools like clear.photo. Background: The Role of Segmentation in Watermark Removal A typical advanced watermark removal pipeline involves: Segmentation: Generating a precise mask that isolates the watermark pixels from the background image content. Inpainting/Restoration: Using the mask to guide an...