Update changelog.

CHANGELOG.rst

@@ -13,7 +13,6 @@ Unreleased
 Added
 ~~~~~
 
-- Option to pass background image to ``utils.io.load_data``.
 - Option to set image resolution with ``hardware.utils.display`` function.
 - Add utility for mask adapter generation in ``lenseless.hardware.fabrication``
 - Option to add simulated background in ``util.dataset``
@@ -28,11 +27,19 @@ Added
 - HFSimulated object for simulating lensless data from ground-truth and PSF.
 - Option to set cache directory for Hugging Face datasets.
 - Option to initialize training with another model.
+- Option to pass background image to ``utils.io.load_data``.
+- Option to use background in ``lensless.eval.benchmark``. 
+- Different techniques to use measured background: direct subtraction, learned subtraction, integrated subtraction, concatenated to input.
+- Learnable background subtraction for classes that derive from ``lensless.recon.trainable_recon.TrainableReconstructionAlgorithm``.
+- Integrated background subtraction object ``lensless.recon.integrated_background.IntegratedBackgroundSub``.
+- Option to concatenate background to input to pre-processor.
+- Add support for datasets with measured background to ``lensless.utils.dataset.HFDataset``.
+
 
 Changed
 ~~~~~~~
 
-- Nothing
+- ``lensless.utils.dataset.HFDataset`` no longer inherits from ``lensless.utils.dataset.DualDataset``.
 
 Bugfix
 ~~~~~~

lensless/eval/benchmark.py

@@ -127,7 +127,7 @@ def benchmark(
             background = None
             lensless = batch[0].to(device)
             lensed = batch[1].to(device)
-            if dataset.measured_bg:
+            if dataset.measured_bg and use_background:
                 background = batch[-1].to(device)
             if dataset.multimask or dataset.random_flip:
                 psfs = batch[2]
@@ -152,7 +152,7 @@ def benchmark(
                     plot=False,
                     save=False,
                     output_intermediate=unrolled_output_factor or pre_process_aux,
-                    background=background if use_background else None,
+                    background=background,
                     **kwargs,
                 )
 

lensless/recon/integrated_background_sub.py

@@ -1,6 +1,6 @@
 # #############################################################################
-# multi_wiener.py
-# ===============
+# integrated_background_sub.py
+# ============================
 # Authors :
 # Eric BEZZAM [[email protected]]
 # Stefan PETERS

lensless/recon/rfft_convolve.py

@@ -146,7 +146,6 @@ def convolve(self, x):
             conv_output = torch.fft.ifftshift(
                 torch.fft.irfft2(
                     torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._H,
-                    # torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._H.to(self._padded_data.device),
                     dim=(-3, -2),
                     s=self._padded_shape[-3:-1],
                 ),
@@ -185,7 +184,6 @@ def deconvolve(self, y):
             deconv_output = torch.fft.ifftshift(
                 torch.fft.irfft2(
                     torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._Hadj,
-                    # torch.fft.rfft2(self._padded_data, dim=(-3, -2)) * self._Hadj.to(self._padded_data.device),
                     dim=(-3, -2),
                     s=self._padded_shape[-3:-1],
                 ),

lensless/recon/utils.py

@@ -316,10 +316,8 @@ def apply_denoiser(
     if mode == "inference":
         with torch.no_grad():
             image = model(image, compensation_output)
-            # image = model(image, compensation_output, background=background)
     elif mode == "train":
         image = model(image, compensation_output)
-        # image = model(image, compensation_output, background=background)
     else:
         raise ValueError("mode must be 'inference' or 'train'")