diff --git a/archs/DualDn_arch.py b/archs/DualDn_arch.py
index 98cc975..f52b989 100644
--- a/archs/DualDn_arch.py
+++ b/archs/DualDn_arch.py
@@ -37,6 +37,7 @@ def __init__(self,
         out_c = 4,  
         c = 64,
         backbone_type = 'Restormer',
+        bgu_ratio = 8,
         bias = False,
         LayerNorm_type = 'BiasFree'
     ):
@@ -44,6 +45,7 @@ def __init__(self,
         super(Raw_Dn, self).__init__()
 
         self.with_noise_map= with_noise_map
+        self.bgu_ratio = bgu_ratio
         if self.with_noise_map:
             self.num = 2
         else:
@@ -83,7 +85,7 @@ def forward(self, in_raw, colormask, wb_matrix, rgb_xyz_matrix, ref='D65', gamma
 
         if ref_sRGB != None and not (ref_sRGB == 0).all(): # Only for inference, to keep consistent with ref_sRGB's color
             in_srgb = torch.clamp(in_srgb, min=1e-6, max=1) # For BGU estimation, there may be overflow, set bound.
-            bgu_gamma = bguFit(in_srgb, ref_sRGB)
+            bgu_gamma = bguFit(in_srgb, ref_sRGB, self.bgu_ratio)
             bgu_srgb = bguSlice(bgu_gamma, in_srgb)
             in_srgb = torch.from_numpy(bgu_srgb).float().cuda().permute(2,0,1).unsqueeze()
 
@@ -100,6 +102,7 @@ def __init__(self,
         out_c = 3, 
         c = 64,
         backbone_type = 'Restormer',
+        bgu_ratio = 8,
         bias = False,
         LayerNorm_type = 'BiasFree'
     ):
@@ -109,6 +112,7 @@ def __init__(self,
         self.down = nn.PixelUnshuffle(downscale_factor=2)
         self.conv_out =  nn.Conv2d(c, out_c, kernel_size=(1, 1), stride=(1, 1), bias=bias)
         self.conv_in = nn.Conv2d(in_c, c, kernel_size=(1, 1), stride=(1, 1), bias=bias)
+        self.bgu_ratio = bgu_ratio
         if backbone_type == 'Restormer':
             self.backbone = Restormer(dim=c, num_blocks = [4,6,6,8], num_refinement_blocks = 4, heads = [1,2,4,8], ffn_expansion_factor = 2.66, bias = bias, LayerNorm_type = LayerNorm_type, dual_pixel_task = False)
         elif backbone_type == 'SwinIR':
@@ -125,7 +129,7 @@ def forward(self, in_raw, colormask, wb_matrix, rgb_xyz_matrix, ref='D65', gamma
 
         if ref_sRGB != None and not (ref_sRGB == 0).all(): # Only for inference, to keep consistent with ref_sRGB's color
             in_srgb = torch.clamp(in_srgb, min=1e-6, max=1) # For BGU estimation, there may be overflow, set bound.
-            bgu_gamma = bguFit(in_srgb, ref_sRGB)
+            bgu_gamma = bguFit(in_srgb, ref_sRGB, self.bgu_ratio)
             bgu_srgb = bguSlice(bgu_gamma, in_srgb)
             in_srgb = torch.from_numpy(bgu_srgb).float().cuda().permute(2,0,1).unsqueeze()
 
@@ -144,6 +148,7 @@ def __init__(self,
         out_c = 3,  
         c = 64,
         backbone_type = 'Restormer',
+        bgu_ratio = 8,
         bias = False,
         LayerNorm_type = 'BiasFree'
     ):
@@ -151,6 +156,7 @@ def __init__(self,
         super(DualDn, self).__init__()
 
         self.with_noise_map= with_noise_map
+        self.bgu_ratio = bgu_ratio
         if self.with_noise_map:
             self.num = 2
         else:
@@ -200,7 +206,7 @@ def forward(self, raw, colormask, wb_matrix, rgb_xyz_matrix, ref='D65', gamma_ty
 
             if ref_sRGB != None and not (ref_sRGB == 0).all(): # Only for inference, to keep consistent with ref_sRGB's color
                 skip_srgb = torch.clamp(skip_srgb, min=1e-6, max=1) # For BGU estimation, there may be overflow, set bound.
-                bgu_gamma = bguFit(skip_srgb, ref_sRGB)
+                bgu_gamma = bguFit(skip_srgb, ref_sRGB, self.bgu_ratio)
                 bgu_srgb = bguSlice(bgu_gamma, skip_srgb)
                 rgb_noise_map = bguSlice(bgu_gamma, rgb_noise_map)
                 skip_srgb = torch.from_numpy(bgu_srgb).float().cuda().permute(2,0,1).unsqueeze(0)
@@ -213,7 +219,7 @@ def forward(self, raw, colormask, wb_matrix, rgb_xyz_matrix, ref='D65', gamma_ty
             
             if ref_sRGB != None and not (ref_sRGB == 0).all(): # Only for inference, to keep consistent with ref_sRGB's color
                 skip_srgb = torch.clamp(skip_srgb, min=1e-6, max=1) # For BGU estimation, there may be overflow, set bound.
-                bgu_gamma = bguFit(skip_srgb, ref_sRGB)
+                bgu_gamma = bguFit(skip_srgb, ref_sRGB, self.bgu_ratio)
                 bgu_srgb = bguSlice(bgu_gamma, skip_srgb)
                 skip_srgb = torch.from_numpy(bgu_srgb).float().cuda().permute(2,0,1).unsqueeze()