diff --git a/IGEV-Stereo/core/utils/augmentor.py b/IGEV-Stereo/core/utils/augmentor.py
index fa81ba9..eb32d04 100644
--- a/IGEV-Stereo/core/utils/augmentor.py
+++ b/IGEV-Stereo/core/utils/augmentor.py
@@ -5,7 +5,7 @@ import os
 import time
 from glob import glob
 from skimage import color, io
-from PIL import Image
+from PIL import Image, ImageEnhance
 
 import cv2
 cv2.setNumThreads(0)
@@ -198,21 +198,40 @@ class SparseFlowAugmentor:
         self.v_flip_prob = 0.1
 
         # photometric augmentation params
-        self.photo_aug = Compose([ColorJitter(brightness=0.3, contrast=0.3, saturation=saturation_range, hue=0.3/3.14), AdjustGamma(*gamma)])
-        self.asymmetric_color_aug_prob = 0.2
+        # self.photo_aug = Compose([ColorJitter(brightness=0.3, contrast=0.3, saturation=saturation_range, hue=0.3/3.14), AdjustGamma(*gamma)])
         self.eraser_aug_prob = 0.5
-        
+
+    def chromatic_augmentation(self, img):
+        random_brightness = np.random.uniform(0.8, 1.2)
+        random_contrast = np.random.uniform(0.8, 1.2)
+        random_gamma = np.random.uniform(0.8, 1.2)
+
+        img = Image.fromarray(img)
+
+        enhancer = ImageEnhance.Brightness(img)
+        img = enhancer.enhance(random_brightness)
+        enhancer = ImageEnhance.Contrast(img)
+        img = enhancer.enhance(random_contrast)
+
+        gamma_map = [
+            255 * 1.0 * pow(ele / 255.0, random_gamma) for ele in range(256)
+        ] * 3
+        img = img.point(gamma_map)  # use PIL's point-function to accelerate this part
+
+        img_ = np.array(img)
+
+        return img_
+
     def color_transform(self, img1, img2):
-        image_stack = np.concatenate([img1, img2], axis=0)
-        image_stack = np.array(self.photo_aug(Image.fromarray(image_stack)), dtype=np.uint8)
-        img1, img2 = np.split(image_stack, 2, axis=0)
+        img1 = self.chromatic_augmentation(img1)
+        img2 = self.chromatic_augmentation(img2)
         return img1, img2
 
     def eraser_transform(self, img1, img2):
         ht, wd = img1.shape[:2]
         if np.random.rand() < self.eraser_aug_prob:
             mean_color = np.mean(img2.reshape(-1, 3), axis=0)
-            for _ in range(np.random.randint(1, 3)):
+            for _ in range(1):
                 x0 = np.random.randint(0, wd)
                 y0 = np.random.randint(0, ht)
                 dx = np.random.randint(50, 100)
diff --git a/IGEV-Stereo/train_stereo.py b/IGEV-Stereo/train_stereo.py
index ad0b8b5..b989b14 100644
--- a/IGEV-Stereo/train_stereo.py
+++ b/IGEV-Stereo/train_stereo.py
@@ -240,8 +240,8 @@ if __name__ == '__main__':
     parser.add_argument('--max_disp', type=int, default=192, help="max disp of geometry encoding volume")
 
     # Data augmentation
-    parser.add_argument('--img_gamma', type=float, nargs='+', default=None, help="gamma range")
-    parser.add_argument('--saturation_range', type=float, nargs='+', default=[0, 1.4], help='color saturation')
+    # parser.add_argument('--img_gamma', type=float, nargs='+', default=None, help="gamma range")
+    # parser.add_argument('--saturation_range', type=float, nargs='+', default=[0, 1.4], help='color saturation')
     parser.add_argument('--do_flip', default=False, choices=['h', 'v'], help='flip the images horizontally or vertically')
     parser.add_argument('--spatial_scale', type=float, nargs='+', default=[-0.2, 0.4], help='re-scale the images randomly')
     parser.add_argument('--noyjitter', action='store_true', help='don\'t simulate imperfect rectification')