DiffSTR: Controlled Diffusion Models for Scene Text Removal

• URL: http://arxiv.org/abs/2410.21721v1
• Date: Tue, 29 Oct 2024 04:20:21 GMT
• Title: DiffSTR: Controlled Diffusion Models for Scene Text Removal
• Authors: Sanhita Pathak, Vinay Kaushik, Brejesh Lall,
• Abstract summary: Scene Text Removal (STR) aims to prevent unauthorized use of text in images. STR faces several challenges, including boundary artifacts, inconsistent texture and color, and preserving correct shadows. We introduce a ControlNet diffusion model, treating STR as an inpainting task. We develop a mask pretraining pipeline to condition our diffusion model.
• Score: 5.790630195329777
• License:
• Abstract: To prevent unauthorized use of text in images, Scene Text Removal (STR) has become a crucial task. It focuses on automatically removing text and replacing it with a natural, text-less background while preserving significant details such as texture, color, and contrast. Despite its importance in privacy protection, STR faces several challenges, including boundary artifacts, inconsistent texture and color, and preserving correct shadows. Most STR approaches estimate a text region mask to train a model, solving for image translation or inpainting to generate a text-free image. Thus, the quality of the generated image depends on the accuracy of the inpainting mask and the generator's capability. In this work, we leverage the superior capabilities of diffusion models in generating high-quality, consistent images to address the STR problem. We introduce a ControlNet diffusion model, treating STR as an inpainting task. To enhance the model's robustness, we develop a mask pretraining pipeline to condition our diffusion model. This involves training a masked autoencoder (MAE) using a combination of box masks and coarse stroke masks, and fine-tuning it using masks derived from our novel segmentation-based mask refinement framework. This framework iteratively refines an initial mask and segments it using the SLIC and Hierarchical Feature Selection (HFS) algorithms to produce an accurate final text mask. This improves mask prediction and utilizes rich textural information in natural scene images to provide accurate inpainting masks. Experiments on the SCUT-EnsText and SCUT-Syn datasets demonstrate that our method significantly outperforms existing state-of-the-art techniques.

Related papers

• ColorMAE: Exploring data-independent masking strategies in Masked AutoEncoders [53.3185750528969]
  Masked AutoEncoders (MAE) have emerged as a robust self-supervised framework. We introduce a data-independent method, termed ColorMAE, which generates different binary mask patterns by filtering random noise. We demonstrate our strategy's superiority in downstream tasks compared to random masking.
  arXiv  Detail & Related papers  (2024-07-17T22:04:00Z)
• Zero-Painter: Training-Free Layout Control for Text-to-Image Synthesis [63.757624792753205]
  We present Zero-Painter, a framework for layout-conditional text-to-image synthesis. Our method utilizes object masks and individual descriptions, coupled with a global text prompt, to generate images with high fidelity.
  arXiv  Detail & Related papers  (2024-06-06T13:02:00Z)
• Mask to reconstruct: Cooperative Semantics Completion for Video-text Retrieval [19.61947785487129]
  Mask for Semantics Completion (MASCOT) based on semantic-based masked modeling. Our MASCOT performs state-of-the-art performance on four major text-video retrieval benchmarks.
  arXiv  Detail & Related papers  (2023-05-13T12:31:37Z)
• StrucTexTv2: Masked Visual-Textual Prediction for Document Image Pre-training [64.37272287179661]
  StrucTexTv2 is an effective document image pre-training framework. It consists of two self-supervised pre-training tasks: masked image modeling and masked language modeling. It achieves competitive or even new state-of-the-art performance in various downstream tasks such as image classification, layout analysis, table structure recognition, document OCR, and information extraction.
  arXiv  Detail & Related papers  (2023-03-01T07:32:51Z)
• MaskSketch: Unpaired Structure-guided Masked Image Generation [56.88038469743742]
  MaskSketch is an image generation method that allows spatial conditioning of the generation result using a guiding sketch as an extra conditioning signal during sampling. We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image. Our results show that MaskSketch achieves high image realism and fidelity to the guiding structure.
  arXiv  Detail & Related papers  (2023-02-10T20:27:02Z)
• SmartBrush: Text and Shape Guided Object Inpainting with Diffusion Model [27.91089554671927]
  Generic image inpainting aims to complete a corrupted image by borrowing surrounding information. By contrast, multi-modal inpainting provides more flexible and useful controls on the inpainted content. We propose a new diffusion-based model named SmartBrush for completing a missing region with an object using both text and shape-guidance.
  arXiv  Detail & Related papers  (2022-12-09T18:36:13Z)
• DiffEdit: Diffusion-based semantic image editing with mask guidance [64.555930158319]
  DiffEdit is a method to take advantage of text-conditioned diffusion models for the task of semantic image editing. Our main contribution is able to automatically generate a mask highlighting regions of the input image that need to be edited.
  arXiv  Detail & Related papers  (2022-10-20T17:16:37Z)
• Layered Depth Refinement with Mask Guidance [61.10654666344419]
  We formulate a novel problem of mask-guided depth refinement that utilizes a generic mask to refine the depth prediction of SIDE models. Our framework performs layered refinement and inpainting/outpainting, decomposing the depth map into two separate layers signified by the mask and the inverse mask. We empirically show that our method is robust to different types of masks and initial depth predictions, accurately refining depth values in inner and outer mask boundary regions.
  arXiv  Detail & Related papers  (2022-06-07T06:42:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.