Update app.py

app.py

@@ -38,7 +38,7 @@ class Generator(nn.Module):
 
         self.exit = nn.Sequential(
             nn.Conv2d(64, 3, 3, 1, 1),
-            nn.Sigmoid()  # output [0,1]
+            nn.Sigmoid()
         )
 
     def forward(self, x):
@@ -87,7 +87,7 @@ def enhance_image(input_image):
 
     output_img = (output * 255).astype(np.uint8)
 
-    # Resize back to original size
+    # Resize back
     output_img = Image.fromarray(output_img)
     output_img = output_img.resize(original_size, Image.BICUBIC)
     output_img = np.array(output_img)
@@ -96,21 +96,31 @@ def enhance_image(input_image):
     # FINAL SAFE POST-PROCESSING
     # ---------------------------
 
-    # Light smoothing
+    # 1. Light smoothing (remove artifacts)
     output_img = cv2.GaussianBlur(output_img, (3, 3), 0)
 
-    # Proper sharpening
-    kernel = np.array([
+    # 2. Mild sharpening (safe)
+    sharpen_kernel = np.array([
         [0, -1, 0],
         [-1, 5, -1],
         [0, -1, 0]
     ])
-    output_img = cv2.filter2D(output_img, -1, kernel)
+    output_img = cv2.filter2D(output_img, -1, sharpen_kernel)
 
-    # Blend with original (VERY IMPORTANT)
+    # 3. Blend with original (VERY IMPORTANT)
     original_np = np.array(img.resize(original_size))
     output_img = cv2.addWeighted(original_np, 0.7, output_img, 0.3, 0)
 
+    # 4. Adaptive contrast (CLAHE - BEST FINAL TOUCH)
+    lab = cv2.cvtColor(output_img, cv2.COLOR_RGB2LAB)
+    l, a, b = cv2.split(lab)
+
+    clahe = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+    l = clahe.apply(l)
+
+    lab = cv2.merge((l, a, b))
+    output_img = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB)
+
     output_img = np.clip(output_img, 0, 255)
 
     # Save for download