diff --git a/models/Model_AMP/Model.py b/models/Model_AMP/Model.py
index f9a53bf..56b3a28 100644
--- a/models/Model_AMP/Model.py
+++ b/models/Model_AMP/Model.py
@@ -490,6 +490,23 @@ class AMPModel(ModelBase):
                         gpu_dst_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_dst*gpu_target_dstm_em - gpu_pred_dst_dst*gpu_target_dstm_em ), axis=[1,2,3])
                     gpu_dst_loss += tf.reduce_mean ( 10*tf.square( gpu_target_dstm - gpu_pred_dst_dstm ),axis=[1,2,3] )
                     gpu_dst_loss += 0.1*tf.reduce_mean(tf.square(gpu_pred_dst_dst_anti_masked-gpu_target_dst_anti_masked),axis=[1,2,3] )
+
+                    if self.options['background_power'] > 0:
+                        bg_factor = self.options['background_power']
+
+                        if self.options['loss_function'] == 'MS-SSIM':
+                            gpu_dst_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
+                            gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
+                        elif self.options['loss_function'] == 'MS-SSIM+L1':
+                            gpu_dst_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0)
+                        else:
+                            if resolution < 256:
+                                gpu_dst_loss +=  bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                            else:
+                                gpu_dst_loss +=  bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                                gpu_dst_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_dst, gpu_pred_dst_dst, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
+                            gpu_dst_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_dst - gpu_pred_dst_dst ), axis=[1,2,3])
+
                     gpu_dst_losses += [gpu_dst_loss]
 
                     if not pretrain:
@@ -510,6 +527,22 @@ class AMPModel(ModelBase):
                             gpu_src_loss += tf.reduce_mean ( 300*tf.abs ( gpu_target_src*gpu_target_srcm_em - gpu_pred_src_src*gpu_target_srcm_em ), axis=[1,2,3])
 
                         gpu_src_loss += tf.reduce_mean ( 10*tf.square( gpu_target_srcm - gpu_pred_src_srcm ),axis=[1,2,3] )
+
+                        if self.options['background_power'] > 0:
+                            bg_factor = self.options['background_power']
+
+                            if self.options['loss_function'] == 'MS-SSIM':
+                                gpu_src_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution)(gpu_target_src, gpu_pred_src_src, max_val=1.0)
+                                gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])
+                            elif self.options['loss_function'] == 'MS-SSIM+L1':
+                                gpu_src_loss += bg_factor * 10 * nn.MsSsim(bs_per_gpu, input_ch, resolution, use_l1=True)(gpu_target_src, gpu_pred_src_src, max_val=1.0)
+                            else:
+                                if resolution < 256:
+                                    gpu_src_loss +=  bg_factor * tf.reduce_mean ( 10*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                                else:
+                                    gpu_src_loss +=  bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/11.6)), axis=[1])
+                                    gpu_src_loss += bg_factor * tf.reduce_mean ( 5*nn.dssim(gpu_target_src, gpu_pred_src_src, max_val=1.0, filter_size=int(resolution/23.2)), axis=[1])
+                                gpu_src_loss += bg_factor * tf.reduce_mean ( 10*tf.square ( gpu_target_src - gpu_pred_src_src ), axis=[1,2,3])
                     else:
                         gpu_src_loss = gpu_dst_loss