update citation and readme

README.md

@@ -1,49 +1,20 @@
-https://towzeur.github.io/QN-Mixer/
 
 
-# Academic Project Page Template
-This is an academic paper project page template.
+The [project page](https://towzeur.github.io/QN-Mixer/) of: [QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction](https://arxiv.org/abs/2402.17951)
 
-Example project pages built using this template are:
-- https://vision.huji.ac.il/spectral_detuning/
-- https://dreamix-video-editing.github.io
-- https://www.vision.huji.ac.il/conffusion/
-- https://www.vision.huji.ac.il/3d_ads/
-- https://www.vision.huji.ac.il/ssrl_ad/
-- https://www.vision.huji.ac.il/deepsim/
+# Abstract
 
+Inverse problems span across diverse fields. In medical contexts, computed tomography (CT) plays a crucial role in reconstructing a patient's internal structure, presenting challenges due to artifacts caused by inherently ill-posed inverse problems. Previous research advanced image quality via post-processing and deep unrolling algorithms but faces challenges, such as extended convergence times with ultra-sparse data. Despite enhancements, resulting images often show significant artifacts, limiting their effectiveness for real-world diagnostic applications. We aim to explore deep second-order unrolling algorithms for solving imaging inverse problems, emphasizing their faster convergence and lower time complexity compared to common first-order methods like gradient descent. In this paper, we introduce QN-Mixer, an algorithm based on the quasi-Newton approach. We use learned parameters through the BFGS algorithm and introduce Incept-Mixer, an efficient neural architecture that serves as a non-local regularization term, capturing long-range dependencies within images. To address the computational demands typically associated with quasi-Newton algorithms that require full Hessian matrix computations, we present a memory-efficient alternative. Our approach intelligently downsamples gradient information, significantly reducing computational requirements while maintaining performance. The approach is validated through experiments on the sparse-view CT problem, involving various datasets and scanning protocols, and is compared with post-processing and deep unrolling state-of-the-art approaches. Our method outperforms existing approaches and achieves state-of-the-art performance in terms of SSIM and PSNR, all while reducing the number of unrolling iterations required.
 
+# Citation
 
-## Start using the template
-To start using the template click on `Use this Template`.
+Please cite our paper if you find it useful for your research.
 
-The template uses html for controlling the content and css for controlling the style. 
-To edit the websites contents edit the `index.html` file. It contains different HTML "building blocks", use whichever ones you need and comment out the rest.  
-
-**IMPORTANT!** Make sure to replace the `favicon.ico` under `static/images/` with one of your own, otherwise your favicon is going to be a dreambooth image of me.
-
-## Components
-- Teaser video
-- Images Carousel
-- Youtube embedding
-- Video Carousel
-- PDF Poster
-- Bibtex citation
-
-## Tips:
-- The `index.html` file contains comments instructing you what to replace, you should follow these comments.
-- The `meta` tags in the `index.html` file are used to provide metadata about your paper 
-(e.g. helping search engine index the website, showing a preview image when sharing the website, etc.)
-- The resolution of images and videos can usually be around 1920-2048, there rarely a need for better resolution that take longer to load. 
-- All the images and videos you use should be compressed to allow for fast loading of the website (and thus better indexing by search engines). For images, you can use [TinyPNG](https://tinypng.com), for videos you can need to find the tradeoff between size and quality.
-- When using large video files (larger than 10MB), it's better to use youtube for hosting the video as serving the video from the website can take time.
-- Using a tracker can help you analyze the traffic and see where users came from. [statcounter](https://statcounter.com) is a free, easy to use tracker that takes under 5 minutes to set up. 
-- This project page can also be made into a github pages website.
-- Replace the favicon to one of your choosing (the default one is of the Hebrew University). 
-- Suggestions, improvements and comments are welcome, simply open an issue or contact me. You can find my contact information at [https://pages.cs.huji.ac.il/eliahu-horwitz/](https://pages.cs.huji.ac.il/eliahu-horwitz/)
-
-## Acknowledgments
-Parts of this project page were adopted from the [Nerfies](https://nerfies.github.io/) page.
-
-## Website License
-<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative Commons License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/88x31.png" /></a><br />This work is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.
+```
+@inproceedings{ayad2024,
+    title={QN-Mixer: A Quasi-Newton MLP-Mixer Model for Sparse-View CT Reconstruction}, 
+    author={Ayad, Ishak and Larue, Nicolas and Nguyen, Maï K.},
+    booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
+    year={2024},
+} 
+```