Update README.md

Author: Topping1

README.md

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-# L1BSR-GUI
-Graphical interface for Self-Supervised SISR for Sentinel-2 L1B Imagery
+# Sentinel-2 Super-Resolution GUI
+
+A simple, user-friendly desktop application to increase the resolution of Sentinel-2 satellite images.
+
+This tool takes the standard 10-meter Blue, Green, Red, and Near-Infrared (NIR) bands and uses a pre-trained AI model to create a higher-resolution, 2x upscaled image. It's designed for users who want to easily enhance their satellite imagery without complex command-line tools.
+
+
+*(Your screenshot here. Just replace the link!)*
+
+***
+
+## TL;DR (For the Impatient)
+
+1.  **Install stuff**: `pip install pyqt5 torch safetensors rasterio pillow numpy`
+2.  **Get the Model**: Download the `REC_Real_L1B.safetensors` file (you'll need to find this from the original project or a trusted source).
+3.  **Run the App**: `python l1bsr_sr_gui_updated.py`
+4.  **Load Data**: Select the model file and your four Sentinel-2 band files (B02, B03, B04, B08).
+5.  **Process**: Click the "Process" button.
+6.  **Save**: Save your beautiful new high-resolution image as a GeoTIFF or a JPG.
+
+***
+
+## Features
+
+*   **2x Super-Resolution**: Upscales 10m Sentinel-2 bands (B02, B03, B04, B08) to a higher resolution.
+*   **Graphical User Interface**: No command line needed! A simple point-and-click interface.
+*   **Interactive Preview**:
+    *   See a large preview of your final, upscaled image.
+    *   **Click and hold** on the preview to instantly see the "before" image for a quick comparison.
+    *   Use the scrollbars to pan across the entire image if it's large.
+*   **Visual Sharpening**: Optionally apply an Unsharp Mask filter to the preview and JPG output to make details pop.
+*   **Flexible Output Formats**:
+    *   **GeoTIFF**: Save the full 4-band (B,G,R,NIR) output with all geographic data preserved. This is the best option for scientific use.
+    *   **JPG + JGW**: Save a visually-appealing 3-band (RGB) image with an accompanying world file (`.jgw`) for use in GIS software.
+
+## Requirements
+
+You will need Python 3 and the following libraries, which can be installed easily using `pip`:
+
+```
+pip install pyqt5 torch safetensors rasterio pillow numpy
+```
+**For GPU Acceleration (Optional but Recommended)**: If you have an NVIDIA graphics card, make sure you install a version of PyTorch that supports CUDA. This will make the processing significantly faster.
+
+## How to Use
+
+1.  **Place the Model File**: For convenience, create a folder named `trained_models` in the same directory as the script and place the `REC_Real_L1B.safetensors` file inside it. The app will find it automatically on startup.
+    ```
+    - your_project_folder/
+      |- l1bsr_sr_gui_updated.py  (this script)
+      |- trained_models/
+         |- REC_Real_L1B.safetensors
+    ```
+    If you place the model elsewhere, you can select it manually using the "Browse..." button.
+
+2.  **Launch the Application**: Run the script from your terminal:
+    ```bash
+    python l1bsr_sr_gui_updated.py
+    ```
+
+3.  **Select Input Bands**: Click the "Select B02...", "Select B03...", etc., buttons to load your four input GeoTIFF files. A small preview of each band will appear. The files must have the same dimensions and geographic projection.
+
+4.  **Process the Image**: Click the big **"Process (2x Super-Resolution)"** button. Wait a few moments (it will be much faster with a GPU). When it's done, the upscaled RGB preview will appear in the output window.
+
+5.  **Preview and Sharpen**:
+    *   Click and hold the mouse on the output image to compare it with the original.
+    *   To make the image look sharper, check the "Sharpen" box and adjust the slider. This only affects the preview and the final JPG, not the scientific GeoTIFF data.
+
+6.  **Save Your Output**:
+    *   Click **"Save as GeoTIFF..."** to save the pure, 4-band scientific data.
+    *   Click **"Save as JPG + JGW..."** to save a visually-ready 3-band image for reports or simple GIS viewing.
+
+## Attribution and License
+
+This application's user interface and workflow are built upon the core model and concepts from the **L1BSR project**. The AI model architecture (`RCAN`) and processing logic are derived from their original work.
+
+*   **Original Repository**: [https://github.com/centreborelli/L1BSR](https://github.com/centreborelli/L1BSR)
+
+This work, like the original it is based on, is licensed under the **GNU General Public License v3.0**.