diff --git a/codes/data/multiscale_dataset.py b/codes/data/multiscale_dataset.py
index 0a3dc0b4..0a96eb20 100644
--- a/codes/data/multiscale_dataset.py
+++ b/codes/data/multiscale_dataset.py
@@ -10,6 +10,9 @@ import torchvision.transforms.functional as F
 
 
 # Reads full-quality images and pulls tiles at regular zoom intervals from them. Only usable for training purposes.
+from data.image_corruptor import ImageCorruptor
+
+
 class MultiScaleDataset(data.Dataset):
     def __init__(self, opt):
         super(MultiScaleDataset, self).__init__()
@@ -20,6 +23,7 @@ class MultiScaleDataset(data.Dataset):
         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['paths'], [1])
+        self.corruptor = ImageCorruptor(opt)
 
     # 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.
@@ -62,13 +66,19 @@ class MultiScaleDataset(data.Dataset):
         img_full = cv2.resize(img_full, (self.hq_size_cap, self.hq_size_cap), interpolation=cv2.INTER_AREA)
         patches_hq = [cv2.resize(img_full, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA)]
         self.recursively_extract_patches(img_full, patches_hq, 1)
+        # Image corruption is applied against the full size image for this dataset.
+        img_corrupted = self.corruptor.corrupt_images([img_full])[0]
+        patches_hq_corrupted = [cv2.resize(img_corrupted, (self.tile_size, self.tile_size), interpolation=cv2.INTER_AREA)]
+        self.recursively_extract_patches(img_corrupted, patches_hq_corrupted, 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_corrupted = [cv2.cvtColor(p, cv2.COLOR_BGR2RGB) for p in patches_hq_corrupted]
         patches_hq = [torch.from_numpy(np.ascontiguousarray(np.transpose(p, (2, 0, 1)))).float() for p in patches_hq]
         patches_hq = torch.stack(patches_hq, dim=0)
-        patches_lq = [torch.nn.functional.interpolate(p.unsqueeze(0), scale_factor=1/self.scale, mode='bilinear').squeeze() for p in patches_hq]
+        patches_hq_corrupted = [torch.from_numpy(np.ascontiguousarray(np.transpose(p, (2, 0, 1)))).float() for p in patches_hq_corrupted]
+        patches_lq = [torch.nn.functional.interpolate(p.unsqueeze(0), scale_factor=1/self.scale, mode='bilinear').squeeze() for p in patches_hq_corrupted]
         patches_lq = torch.stack(patches_lq, dim=0)
 
         d = {'LQ': patches_lq, 'GT': patches_hq, 'GT_path': full_path}
@@ -118,10 +128,14 @@ def _build_multiscale_patch_index_map(depth, ind, node, leaves):
 if __name__ == '__main__':
     opt = {
         'name': 'amalgam',
-        'dataroot': ['F:\\4k6k\\datasets\\ns_images\\imagesets\\images-half'],
+        'paths': ['F:\\4k6k\\datasets\\images\\div2k\\DIV2K_train_HR'],
         'num_scales': 4,
         'scale': 2,
-        'hq_tile_size': 128
+        'hq_tile_size': 128,
+        'fixed_corruptions': ['jpeg'],
+        'random_corruptions': ['gaussian_blur', 'motion-blur', 'noise-5'],
+        'num_corrupts_per_image': 1,
+        'corruption_blur_scale': 5
     }
 
     import torchvision
@@ -133,14 +147,15 @@ if __name__ == '__main__':
         quadrant=2
         print(i)
         o = ds[random.randint(0, len(ds))]
-        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))
         tree_ind = random.randint(0, len(multiscale_tree))
-        node = multiscale_tree[tree_ind]
-        depth = 0
-        while node is not None:
-            torchvision.utils.save_image(v[node.index].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, depth))
-            depth += 1
-            node = node.parent
\ No newline at end of file
+        for k, v in o.items():
+            if 'path' in k:
+                continue
+            depth = 0
+            node = multiscale_tree[tree_ind]
+            #for j, img in enumerate(v):
+            #    torchvision.utils.save_image(img.unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, j))
+            while node is not None:
+                torchvision.utils.save_image(v[node.index].unsqueeze(0), "debug/%i_%s_%i.png" % (i, k, depth))
+                depth += 1
+                node = node.parent
\ No newline at end of file