sr

app.py

@@ -168,7 +168,7 @@ def get_dataset(dataset_name):
         physics_name = 'CT'
         baseline_name = 'DPIR_CT'
     else:
-        available_physics = ['Inpainting', 'MotionBlur_medium', 'MotionBlur_hard',
+        available_physics = ['Inpainting', 'SR' ,'MotionBlur_medium', 'MotionBlur_hard',
                              'GaussianBlur_easy', 'GaussianBlur_medium', 'GaussianBlur_hard']
         physics_name = 'MotionBlur_hard'
         baseline_name = 'DPIR'
@@ -201,7 +201,7 @@ with gr.Blocks(title=title, theme=gr.themes.Glass()) as interface:
 
     ### USER-SPECIFIC VARIABLES
     dataset_placeholder = gr.State(get_dataset_on_DEVICE_STR("Natural"))
-    available_physics_placeholder = gr.State(['Inpainting', 'MotionBlur_medium', 'MotionBlur_hard',
+    available_physics_placeholder = gr.State(['Inpainting', 'SR', 'MotionBlur_medium', 'MotionBlur_hard',
                                               'GaussianBlur_easy', 'GaussianBlur_medium', 'GaussianBlur_hard'])
     # Issue giving directly a `torch.nn.module` to `gr.State(...)` since it has __call__ method
     # Solution: using lambda expression

factories.py

@@ -15,7 +15,7 @@ class PhysicsWithGenerator(torch.nn.Module):
     """Interface between Physics, Generator and Gradio."""
     all_physics = ["Inpainting", "MotionBlur_medium", "MotionBlur_hard",
                    "GaussianBlur_easy", "GaussianBlur_medium", "GaussianBlur_hard",
-                   "MRI", "CT"]
+                   "MRI", "CT", "SR"]
 
     def __init__(self, physics_name: str, device_str: str = "cpu") -> None:
         super().__init__()
@@ -83,6 +83,21 @@ class PhysicsWithGenerator(torch.nn.Module):
                                  "updatable_params_converter": {"sigma": float},
                                  "fixed_params": {"noise_sigma_min": 0.05, "noise_sigma_max": 0.05,
                                                   "blur_sigma": 4.0, "psf_size": 31, "num_channels": 1}}
+        elif self.name == 'SR':
+            self.physics = dinv.physics.Downsampling(img_size=(3, 256, 256), normalize=False, filter="bicubic",
+                                                     factor=4,
+                                                     padding='constant',
+                                                     noise_model=dinv.physics.GaussianNoise(sigma=0.1),
+                                                     device=device_str)
+            list_filters = ["bicubic"]
+            list_factors = [2, 4]
+            self.physics_generator = dinv.physics.generator.DownsamplingGenerator(filters=list_filters, factors=list_factors,
+                                                           device=device_str) + SigmaGenerator(sigma_min=0.0, sigma_max=0.05, device=device_str)
+            self.generator = self.physics_generator  # here do not add noise
+            self.saved_params = {"updatable_params": {"sigma": 0.0, "factor": 4},
+                                 "updatable_params_converter": {"sigma": float, "factor": int},
+                                 "fixed_params": {"noise_sigma_min": 0., "noise_sigma_max": 0.,
+                                                  "list_filters": list_filters, "list_factors": list_factors}}
         elif self.name == "Inpainting":
             sigma = 0.05
             split_ratio = 0.3