diff --git a/codes/data/chunk_with_reference.py b/codes/data/chunk_with_reference.py
index 177a0280..0393a67c 100644
--- a/codes/data/chunk_with_reference.py
+++ b/codes/data/chunk_with_reference.py
@@ -8,6 +8,7 @@ class ChunkWithReference:
     def __init__(self, opt, path):
         self.path = path.path
         self.tiles, _ = util.get_image_paths('img', self.path)
+        self.strict = opt['strict'] if 'strict' in opt.keys() else True
         if 'ignore_first' in opt.keys():
             self.ignore = opt['ignore_first']
             self.tiles = self.tiles[self.ignore:]
@@ -22,11 +23,17 @@ class ChunkWithReference:
         return img
 
     def __getitem__(self, item):
-        centers = torch.load(osp.join(self.path, "centers.pt"))[self.ignore:]
+        centers = torch.load(osp.join(self.path, "centers.pt"))
         ref = self.read_image_or_get_zero(osp.join(self.path, "ref.jpg"))
         tile = self.read_image_or_get_zero(self.tiles[item])
         tile_id = int(osp.splitext(osp.basename(self.tiles[item]))[0])
-        center, tile_width = centers[tile_id]
+        if tile_id in centers.keys():
+            center, tile_width = centers[tile_id]
+        elif self.strict:
+            raise FileNotFoundError(tile_id, self.tiles[item])
+        else:
+            center = torch.tensor([128,128], dtype=torch.long)
+            tile_width = 256
         mask = np.full(tile.shape[:2] + (1,), fill_value=.1, dtype=tile.dtype)
         mask[center[0] - tile_width // 2:center[0] + tile_width // 2, center[1] - tile_width // 2:center[1] + tile_width // 2] = 1
 
diff --git a/codes/scripts/extract_subimages_with_ref.py b/codes/scripts/extract_subimages_with_ref.py
index 9c69318f..86f3070d 100644
--- a/codes/scripts/extract_subimages_with_ref.py
+++ b/codes/scripts/extract_subimages_with_ref.py
@@ -5,8 +5,6 @@ import numpy as np
 import cv2
 from PIL import Image
 import data.util as data_util  # noqa: E402
-import lmdb
-import pyarrow
 import torch.utils.data as data
 from tqdm import tqdm
 import torch
@@ -16,7 +14,7 @@ def main():
     mode = 'single'  # single (one input folder) | pair (extract corresponding GT and LR pairs)
     split_img = False
     opt = {}
-    opt['n_thread'] = 0
+    opt['n_thread'] = 2
     opt['compression_level'] = 90  # JPEG compression quality rating.
     # CV_IMWRITE_PNG_COMPRESSION from 0 to 9. A higher value means a smaller size and longer
     # compression time. If read raw images during training, use 0 for faster IO speed.
@@ -244,7 +242,7 @@ class TiledDataset(data.Dataset):
 
         h, w, c = img.shape
         # Uncomment to filter any image that doesnt meet a threshold size.
-        if min(h,w) < 1024:
+        if min(h,w) < 512:
             return None
         left = 0
         right = w