diff --git a/cloud/image_enhancer.py b/cloud/image_enhancer.py
index d8790bc..0ac8b75 100644
--- a/cloud/image_enhancer.py
+++ b/cloud/image_enhancer.py
@@ -1,23 +1,26 @@
 import cv2
 
 def enhance_image_for_ai(input_path: str) -> str:
-    """Enhances dark areas and contrast using CLAHE without blowing out bright areas."""
+    """Smooths camera sensor noise and brightens dark areas for vision LLMs."""
     img = cv2.imread(input_path)
     if img is None:
         return input_path
 
-    # Convert to LAB color space to modify luminance channel only
-    lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB)
+    # 1. Remove ISO sensor noise (grain)
+    denoised = cv2.fastNlMeansDenoisingColored(img, None, h=7, hColor=7, templateWindowSize=7, searchWindowSize=21)
+
+    # 2. Convert to LAB color space for luminance enhancement
+    lab = cv2.cvtColor(denoised, cv2.COLOR_BGR2LAB)
     l, a, b = cv2.split(lab)
 
-    # Apply CLAHE to Lightness channel
-    clahe = cv2.createCLAHE(clipLimit=3.0, tileGridSize=(8, 8))
+    # 3. Apply softer CLAHE (lower clipLimit prevents noise amplification)
+    clahe = cv2.createCLAHE(clipLimit=1.8, tileGridSize=(8, 8))
     cl = clahe.apply(l)
 
-    # Merge channels and convert back to BGR
+    # 4. Merge channels back
     limg = cv2.merge((cl, a, b))
     enhanced = cv2.cvtColor(limg, cv2.COLOR_LAB2BGR)
 
     enhanced_path = input_path.replace(".jpg", "_enhanced.jpg")
-    cv2.imwrite(enhanced_path, enhanced)
+    cv2.imwrite(enhanced_path, enhanced, [cv2.IMWRITE_JPEG_QUALITY, 90])
     return enhanced_path
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