Update app.py

app.py

@@ -89,16 +89,20 @@ load_network(model, MODEL_NAME, strict=True, param_key='params')
 
 
 
-title = "See More Details"
-description = ''' ### [ECCV 2024] SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution 
+title = "[ECCV 2024] SMFANet: A Lightweight Self-Modulation Feature Aggregation Network for Efficient Image Super-Resolution"
+description = '''  
 
 #### [Mingjun Zheng](https://github.com/Zheng-MJ), [Long Sun](https://github.com/sunny2109), [Jiangxin Dong](https://scholar.google.com/citations?user=ruebFVEAAAAJ&hl=zh-CN&oi=ao), and [Jinshan Pan](https://jspan.github.io/)
 
 #### [IMAG Lab](https://imag-njust.net/), Nanjing University of Science and Technology
 
 
-####  Drag the slider on the super-resolution image left and right to see the changes in the image details. SeemoRe performs x4 upscaling on the input image.
+<p align="center">
+  <img width="800" src="assets/smfanet_arch.png">
+</p>
+***Network architecture of the proposed SMFANet**. The proposed s SMFANet consists of a shallow feature extraction module, feature modulation blocks, and a lightweight image reconstruction module. Feature modulation block contains one self-modulation feature aggregation (SMFA) module and one partial convolution-based feed-forward network (PCFN).*
 
+####  Drag the slider on the super-resolution image left and right to see the changes in the image details. SeemoRe performs x4 upscaling on the input image.
 <br>
 
 <code>