Update README.md

README.md

@@ -3,49 +3,80 @@
   <p>"Visualisation of the proposed FusionBooster when applied to the infrared and visilble image fusion approach task."</p>
 </div>
 
-# FusionBooster
+### 1 FusionBooster
 This is the offical implementation for the paper titled "FusionBooster: A Unified Image Fusion Boosting Paradigm".
 
 [Paper](https://arxiv.org/abs/2305.05970)
 
 
-## Environment
+### 2 Environment
 ```
 python 3.7.3
 torch 1.9.0
 scipy 1.2.0
 ```
-## Test (End to end, you provide two source images, we directly output the enhanced fused image)
-**(If you want to report this result, please mark our mehtod as FusionBooster-MU)**
+### 3 Test (End to end, you provide two source images, we directly output the enhanced fused image)
 
-Backbone: MUFusion
 
-### For the IVIF task:
+Backbone: MUFusion (If you want to report this result, please mark our mehtod as FusionBooster-MU)
+
+#### 3.1 For the IVIF task:
 ```
 python test_e2e_rgb.py
 ```
 
-## Test (Booster Only, you provide the initial fused image, we enhance it)
-To use our pre-trained FusionBooster to boost an arbitary IVIF method (e.g., DDcGAN):
+#### 3.2 For the MEIF task:
+
+```
+python xxx.py
+```
+
+#### 3.3 For the MFIF task:
+
+```
+python xxx.py
+```
+
+### 4 Test (Booster Only, you provide the initial fused image, we enhance it)
+
+
+
+To use our pre-trained FusionBooster to boost an arbitary method:
+
+#### 4.1 For the IVIF task (Backbone: DDcGAN)
+
+```
+python test_booster_only_rgb.py
+```
+
+#### 4.2 For the MEIF task (Backbone: )
 
 ```
 python test_booster_only_rgb.py
 ```
 
-You can modify the path in the "test_booster_only.py" file, to enhance your own fusion results. 
+#### 4.3 For the MFIF task (Backbone: )
+
+```
+python test_booster_only_rgb.py
+```
+
+You can modify the path in the "test_booster_only_xxxx.py" file, to enhance your own fusion results. 
+
 
-## Announcement
+### Announcement
+- 2024-10-14 The code for end-to-end boosting source images (IVIF) is now available. ("test_e2e_rgb.py").
 - 2024-10-14 The code for boosting an arbitary method is available ("test_booster_only.py").
 - 2024-10-1 Because some of the fusion methods are realised using the tensorflow framework. Our FusionBooster demo will be implemented based on the [MUFusion](https://github.com/AWCXV/MUFusion). You can always use our "detached booster" to enhance your own fusion results. 
 - 2024-9-30 This work has been accepted by IJCV.
 
-## Highlight
+### Highlight
 - We devise an image fusion booster by analysing the quality of the initial fusion results by means of a dedicated Information Probe.
 - The proposed FusionBooster is a general enhancer, which can be applied to various image fusion methods, e.g., traditional or learning-based algorithms, irrespective of the type of fusion task.
 - In a new divide-and-conquer image fusion paradigm, the results of the analysis performed by the Information Probe guide the refinement of the fused image.
 - The proposed FusionBooster significantly enhances the performance of the SOTA fusion methods and downstream detection tasks, with only a slight increase in the computational overhead.
 
-## Citation
+### Citation
 If this work is helpful to you, please cite it as:
 ```
 @article{cheng2024fusionbooster,