diff --git a/readme.md b/readme.md
index 7e583a5..e4accf7 100644
--- a/readme.md
+++ b/readme.md
@@ -1,43 +1,36 @@
-# Vahadane论文normalization
 
 
+### Structure-Preserving Color Normalization and Sparse Stain Separation for Histological Images 
+(Vahadane normalization)
 
-### 简要说明
+Abhishek Vahadane*, Tingying Peng*, Amit Sethi, Shadi Albarqouni, Lichao Wang, Maximilian Baust, Katja Steiger, Anna Melissa Schlitter, Irene Esposito, and Nassir Navab
 
-代码支持python3，未进行2、3兼容检查。
 
-核心代码在`vahadane.py`。
+code: python3
 
-需要额外安装的库有：spams, opencv。我spams安装的时候遇到一些问题，但是因为我的mac的原因。直接使用pip安装spams。opencv可自己找相应操作系统下的教程安装。
+code name: vahadane.py
 
-如果你装了jupyter notebook，可以运行一下main.ipynb，这里面是一个写好了的demo。
+Abstract—Staining and scanning of tissue samples for microscopic examination is fraught with undesirable color variations
+arising from differences in raw materials and manufacturing
+techniques of stain vendors, staining protocols of labs, and color
+responses of digital scanners. When comparing tissue samples,
+color normalization and stain separation of the tissue images can
+be helpful for both pathologists and software. Techniques that
+are used for natural images fail to utilize structural properties of
+stained tissue samples and produce undesirable color distortions.
+The stain concentration cannot be negative. Tissue samples are
+stained with only a few stains and most tissue regions are characterized by at most one effective stain. We model these physical
+phenomena that define the tissue structure by first decomposing
+images in an unsupervised manner into stain density maps that
+are sparse and non-negative. For a given image, we combine its
+stain density maps with stain color basis of a pathologist-preferred
+target image, thus altering only its color while preserving its
+structure described by the maps. Stain density correlation with
+ground truth and preference by pathologists were higher for
+images normalized using our method when compared to other
+alternatives. We also propose a computationally faster extension
+of this technique for large whole-slide images that selects an
+appropriate patch sample instead of using the entire image to
+compute the stain color basis.
 
-Report.md是我写给学姐看的，分析了一下参数选择的效果。可以不看。
-
-
-
-### 使用方法
-
-1 首先调用`utils.py`里的`read_image`函数读取图片。这个函数将输入图片从opencv默认的BGR转成RGB，并且对整体颜色进行了一些加强。读出来的图像矩阵是$n*m*3$的。
-
-2 新建一个vahadane对象。两个LAMBDA已经调到最优。fast_mode是是否快速分解，getH_mode也会影响分解速度。ITER是getW时的一个参数。可以都试一下看看效果。
-
-```python
-vhd = vahadane(LAMBDA1=0.01, LAMBDA2=0.01, fast_mode=1, getH_mode=0, ITER=50)
-vhd.show_config()
-```
-
-3 矩阵分解
-
-```python
-Ws, Hs = vhd.stain_separate(source_image)
-vhd.fast_mode=0;vhd.getH_mode=0;  # 在获取target的分解时，为了更精确，不用fast模式
-Wt, Ht = vhd.stain_separate(target_image)
-```
-
-4 合成target
-
-```python
-img = vhd.SPCN(source_image, Ws, Hs, Wt, Ht)
-```