diff --git a/src/gui/tabs/training_tab.py b/src/gui/tabs/training_tab.py
index 3c2ca9e..257d3c2 100644
--- a/src/gui/tabs/training_tab.py
+++ b/src/gui/tabs/training_tab.py
@@ -34,6 +34,7 @@ from PySide6.QtWidgets import (
 from src.database.db_manager import DatabaseManager
 from src.model.yolo_wrapper import YOLOWrapper
 from src.utils.config_manager import ConfigManager
+from src.utils.image import convert_grayscale_to_rgb_preserve_range
 from src.utils.logger import get_logger
 
 
@@ -1361,7 +1362,10 @@ class TrainingTab(QWidget):
             dst.parent.mkdir(parents=True, exist_ok=True)
             try:
                 with PILImage.open(src) as img:
-                    rgb_img = img.convert("RGB")
+                    if len(img.getbands()) == 1:
+                        rgb_img = convert_grayscale_to_rgb_preserve_range(img)
+                    else:
+                        rgb_img = img.convert("RGB")
                     rgb_img.save(dst)
             except Exception as exc:
                 logger.warning(f"Failed to convert {src} to RGB: {exc}")
diff --git a/src/model/yolo_wrapper.py b/src/model/yolo_wrapper.py
index fc4ac0c..f165c7b 100644
--- a/src/model/yolo_wrapper.py
+++ b/src/model/yolo_wrapper.py
@@ -10,7 +10,7 @@ import torch
 from PIL import Image
 import tempfile
 import os
-import numpy as np
+from src.utils.image import convert_grayscale_to_rgb_preserve_range
 from src.utils.logger import get_logger
 
 
@@ -234,38 +234,20 @@ class YOLOWrapper:
                 try:
                     with Image.open(source_path) as img:
                         if len(img.getbands()) == 1:
-                            grayscale = np.array(img)
-                            if grayscale.ndim == 3:
-                                grayscale = grayscale[:, :, 0]
-                            original_dtype = grayscale.dtype
-                            grayscale = grayscale.astype(np.float32)
+                            rgb_img = convert_grayscale_to_rgb_preserve_range(img)
+                        else:
+                            rgb_img = img.convert("RGB")
 
-                            if np.issubdtype(original_dtype, np.integer):
-                                dtype_info = np.iinfo(original_dtype)
-                                denom = float(max(dtype_info.max, 1))
-                            else:
-                                max_val = (
-                                    float(grayscale.max()) if grayscale.size else 0.0
-                                )
-                                denom = max(max_val, 1.0)
-
-                            grayscale = np.clip(grayscale / denom, 0.0, 1.0)
-                            grayscale_u8 = (grayscale * 255.0).round().astype(np.uint8)
-                            rgb_arr = np.repeat(grayscale_u8[:, :, None], 3, axis=2)
-                            rgb_img = Image.fromarray(rgb_arr, mode="RGB")
-
-                            suffix = source_path.suffix or ".png"
-                            tmp = tempfile.NamedTemporaryFile(
-                                suffix=suffix, delete=False
-                            )
-                            tmp_path = tmp.name
-                            tmp.close()
-                            rgb_img.save(tmp_path)
-                            cleanup_path = tmp_path
-                            logger.info(
-                                f"Converted single-channel image {source_path} to RGB for inference at {tmp_path}"
-                            )
-                            return tmp_path, cleanup_path
+                        suffix = source_path.suffix or ".png"
+                        tmp = tempfile.NamedTemporaryFile(suffix=suffix, delete=False)
+                        tmp_path = tmp.name
+                        tmp.close()
+                        rgb_img.save(tmp_path)
+                        cleanup_path = tmp_path
+                        logger.info(
+                            f"Converted image {source_path} to RGB for inference at {tmp_path}"
+                        )
+                        return tmp_path, cleanup_path
                 except Exception as convert_error:
                     logger.warning(
                         f"Failed to preprocess {source_path} as RGB, continuing with original file: {convert_error}"
diff --git a/src/utils/image.py b/src/utils/image.py
index 9dc867d..69139cd 100644
--- a/src/utils/image.py
+++ b/src/utils/image.py
@@ -289,3 +289,40 @@ class Image:
     def __str__(self) -> str:
         """String representation of the Image object."""
         return self.__repr__()
+
+
+def convert_grayscale_to_rgb_preserve_range(
+    pil_image: PILImage.Image,
+) -> PILImage.Image:
+    """Convert a single-channel PIL image to RGB while preserving dynamic range.
+
+    Args:
+        pil_image: Single-channel PIL image (e.g., 16-bit grayscale).
+
+    Returns:
+        PIL Image in RGB mode with intensities normalized to 0-255.
+    """
+
+    if pil_image.mode == "RGB":
+        return pil_image
+
+    grayscale = np.array(pil_image)
+    if grayscale.ndim == 3:
+        grayscale = grayscale[:, :, 0]
+
+    original_dtype = grayscale.dtype
+    grayscale = grayscale.astype(np.float32)
+
+    if grayscale.size == 0:
+        return PILImage.new("RGB", pil_image.size, color=(0, 0, 0))
+
+    if np.issubdtype(original_dtype, np.integer):
+        denom = float(max(np.iinfo(original_dtype).max, 1))
+    else:
+        max_val = float(grayscale.max())
+        denom = max(max_val, 1.0)
+
+    grayscale = np.clip(grayscale / denom, 0.0, 1.0)
+    grayscale_u8 = (grayscale * 255.0).round().astype(np.uint8)
+    rgb_arr = np.repeat(grayscale_u8[:, :, None], 3, axis=2)
+    return PILImage.fromarray(rgb_arr, mode="RGB")