diff --git a/codes/data/multiscale_dataset.py b/codes/data/multiscale_dataset.py
new file mode 100644
index 00000000..97c7f167
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+++ b/codes/data/multiscale_dataset.py
@@ -0,0 +1,126 @@
+import random
+import numpy as np
+import cv2
+import torch
+import torch.utils.data as data
+import data.util as util
+from PIL import Image, ImageOps
+from io import BytesIO
+import torchvision.transforms.functional as F
+
+
+# Reads full-quality images and pulls tiles at regular zoom intervals from them. Only usable for training purposes.
+class MultiScaleDataset(data.Dataset):
+    def __init__(self, opt):
+        super(MultiScaleDataset, self).__init__()
+        self.opt = opt
+        self.data_type = 'img'
+        self.tile_size = self.opt['hq_tile_size']
+        self.num_scales = self.opt['num_scales']
+        self.hq_size_cap = self.tile_size * 2 ** self.num_scales
+        self.scale = self.opt['scale']
+        self.paths_hq, self.sizes_hq = util.get_image_paths(self.data_type, opt['dataroot'], [1])
+
+    # Selects the smallest dimension from the image and crops it randomly so the other dimension matches. The cropping
+    # offset from center is chosen on a normal probability curve.
+    def get_square_image(self, image):
+        h, w, _ = image.shape
+        if h == w:
+            return image
+        offset = max(min(np.random.normal(scale=.3), 1.0), -1.0)
+        if h > w:
+            diff = h - w
+            center = diff // 2
+            top = int(center + offset * (center - 2))
+            return image[top:top+w, :, :]
+        else:
+            diff = w - h
+            center = diff // 2
+            left = int(center + offset * (center - 2))
+            return image[:, left:left+h, :]
+
+    def recursively_extract_patches(self, input_img, result_list, depth):
+        if depth > self.num_scales:
+            return
+        patch_size = self.hq_size_cap // (2 ** depth)
+        # First pull the four sub-patches.
+        patches = [input_img[:patch_size, :patch_size],
+                   input_img[:patch_size, patch_size:],
+                   input_img[patch_size:, :patch_size],
+                   input_img[patch_size:, patch_size:]]
+        result_list.extend([cv2.resize(p, (self.tile_size, self.tile_size), interpolation=cv2.INTER_LINEAR) for p in patches])
+        for p in patches:
+            self.recursively_extract_patches(p, result_list, depth+1)
+
+    def __getitem__(self, index):
+        # get full size image
+        full_path = self.paths_hq[index % len(self.paths_hq)]
+        img_full = util.read_img(None, full_path, None)
+        img_full = util.channel_convert(img_full.shape[2], 'RGB', [img_full])[0]
+        img_full = util.augment([img_full], True, True)[0]
+        img_full = self.get_square_image(img_full)
+        img_full = cv2.resize(img_full, (self.hq_size_cap, self.hq_size_cap), interpolation=cv2.INTER_LINEAR)
+        patches_hq = [cv2.resize(img_full, (self.tile_size, self.tile_size), interpolation=cv2.INTER_LINEAR)]
+        self.recursively_extract_patches(img_full, patches_hq, 1)
+
+        # BGR to RGB, HWC to CHW, numpy to tensor
+        if patches_hq[0].shape[2] == 3:
+            patches_hq = [cv2.cvtColor(p, cv2.COLOR_BGR2RGB) for p in patches_hq]
+        patches_hq = [torch.from_numpy(np.ascontiguousarray(np.transpose(p, (2, 0, 1)))).float() for p in patches_hq]
+        patches_lq = [torch.nn.functional.interpolate(p.unsqueeze(0), scale_factor=1/self.scale, mode='bilinear').squeeze() for p in patches_hq]
+
+        d = {'LQ': patches_lq, 'HQ': patches_hq, 'GT_path': full_path}
+        return d
+
+    def __len__(self):
+        return len(self.paths_hq)
+
+
+def build_multiscale_patch_index_map(depth):
+    if depth < 0:
+        return
+    recursive_list = []
+    map = (0, recursive_list)
+    _build_multiscale_patch_index_map(depth, 1, recursive_list)
+    return map
+
+
+def _build_multiscale_patch_index_map(depth, ind, recursive_list):
+    if depth <= 0:
+        return ind
+    patches = [(ind+i, []) for i in range(4)]
+    recursive_list.extend(patches)
+    ind += 4
+    for _, p in patches:
+        ind = _build_multiscale_patch_index_map(depth-1, ind, p)
+    return ind
+
+
+if __name__ == '__main__':
+    opt = {
+        'name': 'amalgam',
+        'dataroot': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\images'],
+        'num_scales': 4,
+        'scale': 2,
+        'hq_tile_size': 128
+    }
+
+    import torchvision
+    ds = MultiScaleDataset(opt)
+    import os
+    os.makedirs("debug", exist_ok=True)
+    multiscale_map = build_multiscale_patch_index_map(4)
+    for i in range(900, len(ds)):
+        quadrant=2
+        print(i)
+        o = ds[i]
+        k = 'HQ'
+        v = o['HQ']
+        #for j, img in enumerate(v):
+        #    torchvision.utils.save_image(img.unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, j))
+        torchvision.utils.save_image(v[0].unsqueeze(0), "debug/%i_%s_0.png" % (i, k))
+        map_tuple = multiscale_map[1][quadrant]
+        while map_tuple[1]:
+            ind = map_tuple[0]
+            torchvision.utils.save_image(v[ind].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, ind+1))
+            map_tuple = map_tuple[1][quadrant]
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