Upload README.md

README.md

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-<div align="center">
-  
-  ## [Enhanced Generative Structure Prior for Chinese Text Image Super-Resolution](https://arxiv.org/pdf/2508.07537)
-
-[Xiaoming Li](https://csxmli2016.github.io/), [Wangmeng Zuo](https://scholar.google.com/citations?hl=en&user=rUOpCEYAAAAJ&view_op=list_works), [Chen Change Loy](https://www.mmlab-ntu.com/person/ccloy/)
-
-S-Lab, Nanyang Technological University
-
-</div>
-
-
-### 👷 The whole framework:
-<div align="center">
-<img src="./Imgs/pipeline.png" width="800px">
-</div>
-
-
-
-<!-- 
-<p align="justify">Faithful text image super-resolution (SR) is challenging because each character has a unique structure and usually exhibits diverse font styles and
-layouts. While existing methods primarily focus on English text, less attention has been paid to more complex scripts like Chinese. In this paper, we introduce a high-quality text image SR framework designed to restore the precise strokes of low-resolution (LR) Chinese characters. Unlike methods that rely on character recognition priors to regularize the SR task, we propose a novel structure prior that offers structure-level guidance to enhance visual quality. Our framework incorporates this structure prior within a StyleGAN model, leveraging its generative capabilities for restoration. To maintain the integrity of character structures while accommodating various font styles and layouts, we implement a codebook-based mechanism that restricts the generative space of StyleGAN. Each code in the codebook represents the structure of a specific character, while the vector $w$ in StyleGAN controls the character's style, including typeface, orientation, and location. Through the collaborative interaction between the codebook and style, we generate a high-resolution structure prior that aligns with LR characters both spatially and structurally. Experiments demonstrate that this structure prior provides robust, character-specific guidance, enabling the accurate restoration of clear strokes in degraded characters, even for real-world LR Chinese text with irregular layouts.  </p>
--->
-
-### 👷 Character Structure Prior Pretraining:
-<div align="center">
-<img src="./Imgs/prior.gif" width="800px">
-</div>
-
-
-## 🔔 MARCONet 🆚 MARCONet++
-> - MARCONet is designed for **regular character layout** only. See details of [MARCONet](https://github.com/csxmli2016/MARCONet).
-> - MARCONet++ has more accurate alignment between character structural prior (green structure) and the degraded image.
-<div align="center">
-<img src="./Imgs/marconet_vs_marconetplus.jpg" width="800px">
-</div>
-
-
-
-## 📋 TODO
-- [x] Release the inference code and model.
-- [ ] Release the training code (no plans to release for now). 
-
-
-## 🚶 Getting Started
-
-```
-git clone https://github.com/csxmli2016/MARCONetPlusPlus
-cd MARCONetPlusPlus
-conda create -n mplus python=3.8 -y
-conda activate mplus
-pip install -r requirements.txt
-```
-
-## 🚶 Inference
-Download the pre-trained models
-```
-python utils/download_github.py
-```
-
-and run for restoring **text lines:**
-```
-CUDA_VISIBLE_DEVICES=0 python test_marconetplus.py -i ./Testsets/LR_TextLines -a -s
-```
-or run for restoring **the whole text image:**
-```
-CUDA_VISIBLE_DEVICES=0 python test_marconetplus.py -i ./Testsets/LR_Whole -b -s -f 2
-```
-
-```
-# Parameters:
--i: --input_path, default: ./Testsets/LR_TextLines or ./Testsets/LR_TextWhole
--o: --output_path, default: None will automatically make the saving dir with the format of '[LR path]_TIME_MARCONetPlus'
--a: --aligned, if the input is text lines, use -a; otherwise, the input is the whole text image and needs text line detection, do not use -a
--b: --bg_sr, when restoring the whole text images, use -b to restore the background region with BSRGAN. Without -b, background will keep the same as input
--f: --factor_scale, default: 2. When restoring the whole text images, use -f to define the scale factor of output
--s: --save_text, if you want to see the details of prior alignment, predicted characters, and locations, use -s 
-```
-
-
-
-## 🏃 Restoring Real-world Chinese Text Images
-> - We use [BSRGAN](https://github.com/cszn/BSRGAN) to restore the background region.
-> - The parameters are tested on an NVIDIA A100 GPU (40G).
-> - ⚠️ If the inference speed is slow, this is caused by the large size of the input text image or the large factor_scale. You can resize it based on your needs.
-
-[<img src="Imgs/whole_1.jpg" height="270px"/>](https://imgsli.com/NDA2MDUw) [<img src="Imgs/whole_2.jpg" height="270px"/>](https://imgsli.com/NDA2MDYw) 
-
-[<img src="Imgs/whole_3.jpg" height="540px"/>](https://imgsli.com/NDA2MTE0) [<img src="Imgs/whole_4.jpg" height="540px" width="418px"/>](https://imgsli.com/NDA2MDYy) 
-
-
-## 🏃 Restoring detected text line
-
-<img src="Imgs/text_line_sr.jpg" width="800px"/>
-
-
-<details><summary><h2>🏃 Style w interpolation from three characters with different styles</h2></summary>
-  <img src="./Imgs/w-interpolation.gif" width="400px">
-</details>
-
-
-
-
-## ‼️ Failure Case
-Despite its high-fidelity performance, MARCONet++ still struggles in some real-world scenarios as it highly relies on:
-
-- Real world character **Recognition** on complex degraded text images
-- Real world character **Detection** on complex degraded text images
-- Text line detection and segmentation
-- Domain gap between our synthetic and real-world text images
-
-
-<img src="./Imgs/failure_case.jpg"  width="800px">
-
-> 🍒 Restoring complex character with high fidelity under such conditions has significant challenges.
-We have also explored various approaches, such as training OCR models with Transformers and using YOLO or Transformer-based methods for character detection, but these methods generally encounter the same issues. 
-We encourage any potential collaborations to jointly tackle this challenge and advance robust, high-fidelity text restoration.
-
-
-## 📎 RealCE-1K benchmark
-To quantitatively evaluate on real-world Chinese text line images, we curate a benchmark by filtering the [RealCE](https://github.com/mjq11302010044/Real-CE) test set to exclude images containing multiple text lines or inaccurate annotations, thereby constructing a Chinese text SR benchmark (see Section IV.B of our paper). You can download the RealCE-1K benchmark from [here](https://github.com/csxmli2016/MARCONetPlusPlus/releases/download/v1/RealCE-1K.zip).
-
-## 🍺 Acknowledgement
-This project is built based on the excellent [KAIR](https://github.com/cszn/KAIR) and [RealCE](https://github.com/mjq11302010044/Real-CE).
-
-## ©️ License
-This project is licensed under <a rel="license" href="https://github.com/csxmli2016/MARCONetPlusPlus/blob/main/LICENSE">NTU S-Lab License 1.0</a>. Redistribution and use should follow this license.
-
-
-## 🍻 Citation
-```
-@article{li2025marconetplus,
-  author = {Li, Xiaoming and Zuo, Wangmeng and Loy, Chen Change},
-  title = {Enhanced Generative Structure Prior for Chinese Text Image Super-Resolution},
-  journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
-  year = {2025}
-}
-
-@InProceedings{li2023marconet,
-  author = {Li, Xiaoming and Zuo, Wangmeng and Loy, Chen Change},
-  title = {Learning Generative Structure Prior for Blind Text Image Super-resolution},
-  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
-  year = {2023}
-}
-```
-
-
+title: MARCONet++
+emoji: 📝
+colorFrom: blue
+colorTo: green
+sdk: gradio
+sdk_version: 4.44.1
+app_file: app.py
+pinned: true