Add DnCNN with noise level input

docs/source/examples.rst

@@ -6,7 +6,7 @@ Usage Examples
 .. toctree::
    :maxdepth: 1
 
-.. include:: include/exampledepend.rst
+.. include:: exampledepend.rst
 
 
 Organized by Application
@@ -73,6 +73,7 @@ Miscellaneous
    examples/denoise_tv_pgm
    examples/denoise_tv_multi
    examples/denoise_cplx_tv_pdhg
+   examples/denoise_dncnn_universal
    examples/video_rpca_admm
 
 

docs/source/references.bib

@@ -23,6 +23,19 @@ @Article {almeida-2013-deconvolving
   doi =		 {10.1109/TIP.2013.2258354}
 }
 
+@Article {balke-2022-scico,
+  author =	 {Thilo Balke and Fernando Davis and Cristina
+                  Garcia-Cardona and Soumendu Majee and Michael McCann
+                  and Luke Pfister and Brendt Wohlberg},
+  title =	 {Scientific Computational Imaging Code ({SCICO})},
+  journal =	 {Journal of Open Source Software},
+  year =	 2022,
+  volume =	 7,
+  number =	 78,
+  pages =	 4722,
+  doi =		 {10.21105/joss.04722}
+}
+
 @Article {barzilai-1988-stepsize,
   author =	 {Jonathan Barzilai and Jonathan M. Borwein},
   title =	 {Two-point step size gradient methods},
@@ -70,7 +83,8 @@ @InCollection{beck-2010-gradient
   publisher =	 {Cambridge University Press},
   year =	 2010,
   doi =		 {10.1017/CBO9780511804458.003},
-  url =          {http://www.math.tau.ac.il/~teboulle/papers/gradient_chapter.pdf}
+  url =
+                  {http://www.math.tau.ac.il/~teboulle/papers/gradient_chapter.pdf}
 }
 
 @Book {beck-2017-first,
@@ -331,10 +345,10 @@ @Article {kamilov-2022-plug
                   T. Buzzard and Brendt Wohlberg},
   title =	 {Plug-and-Play Methods for Integrating Physical and
                   Learned Models in Computational Imaging},
-  journal =      {IEEE Signal Processing Magazine},
+  journal =	 {IEEE Signal Processing Magazine},
   year =	 2022,
   eprint =	 {arXiv:2203.17061},
-  note =         {To appear.}
+  note =	 {To appear.}
 }
 
 @Article {liu-2018-first,
@@ -361,7 +375,7 @@ @Article {maggioni-2012-nonlocal
   number =	 1,
   pages =	 {119--133},
   year =	 2012,
-  doi =          {10.1109/TIP.2012.2210725}
+  doi =		 {10.1109/TIP.2012.2210725}
 }
 
 @InProceedings {makinen-2019-exact,
@@ -414,7 +428,7 @@ @Book {nocedal-2006-numerical
 
 @Book {paganin-2006-coherent,
   doi =		 {10.1093/acprof:oso/9780198567288.001.0001},
-  isbn =	 {9780198567288},
+  isbn =	 9780198567288,
   year =	 2006,
   month =	 Jan,
   publisher =	 {Oxford University Press},
@@ -481,19 +495,6 @@ @Article {sauer-1993-local
   doi =		 {10.1109/78.193196}
 }
 
-@Article {balke-2022-scico,
-  author =	 {Thilo Balke and Fernando Davis and Cristina
-                  Garcia-Cardona and Soumendu Majee and Michael McCann
-                  and Luke Pfister and Brendt Wohlberg},
-  title =	 {Scientific Computational Imaging Code ({SCICO})},
-  journal =	 {Journal of Open Source Software},
-  year =	 {2022},
-  volume =	 {7},
-  number =	 {78},
-  pages =	 {4722},
-  doi =		 {10.21105/joss.04722}
-}
-
 @Article {soulez-2016-proximity,
   author =	 {Ferr{\'{e}}ol Soulez and {\'{E}}ric Thi{\'{e}}baut
                   and Antony Schutz and Andr{\'{e}} Ferrari and
@@ -506,7 +507,6 @@ @Article {soulez-2016-proximity
   volume =	 55,
   number =	 26,
   pages =	 {7412--7421}
-
 }
 
 @Article {sreehari-2016-plug,
@@ -541,7 +541,7 @@ @Article {valkonen-2014-primal
   journal =	 {Inverse Problems},
   volume =	 30,
   number =	 5,
-  pages =	 {055012},
+  pages =	 055012,
   year =	 2014,
   doi =		 {10.1088/0266-5611/30/5/055012}
 }
@@ -609,6 +609,20 @@ @Article {zhang-2017-dncnn
   pages =	 {3142--3155}
 }
 
+@Article {zhang-2021-plug,
+  author =	 {Zhang, Kai and Li, Yawei and Zuo, Wangmeng and
+                  Zhang, Lei and Van Gool, Luc and Timofte, Radu},
+  title =	 {Plug-and-Play Image Restoration With Deep Denoiser
+                  Prior},
+  journal =	 {IEEE Transactions on Pattern Analysis and Machine
+                  Intelligence},
+  year =	 2022,
+  volume =	 44,
+  number =	 10,
+  doi =		 {10.1109/TPAMI.2021.3088914},
+  pages =	 {6360--6376}
+}
+
 @Article {zhou-2006-adaptive,
   author =	 {Bin Zhou and Li Gao and Yu-Hong Dai},
   title =	 {Gradient Methods with Adaptive Step-Sizes},

docs/source/team.rst

@@ -43,3 +43,4 @@ Contributors
 - `Yanpeng Yuan <https://github.com/yanpeng7>`_ (ASTRA interface improvements)
 - `Saurav Maheshkar <https://github.com/SauravMaheshkar>`_ (Improvements to pre-commit configuration)
 - `Andrew Leong <https://scholar.google.com/citations?user=-2wRWbcAAAAJ&hl=en>`_ (Improvements to optics module documentation)
+- `Weijie Gan <https://github.com/wjgancn>`_ (Non-blind variant of DnCNN)

examples/scripts/README.rst

@@ -78,6 +78,8 @@ Miscellaneous
       Comparison of Optimization Algorithms for Total Variation Denoising
    `denoise_cplx_tv_pdhg.py <denoise_cplx_tv_pdhg.py>`_
       Complex Total Variation Denoising
+   `denoise_dncnn_universal.py <denoise_dncnn_universal.py>`_
+      Comparison of DnCNN Variants for Image Denoising
    `video_rpca_admm.py <video_rpca_admm.py>`_
       Video Decomposition via Robust PCA
 

examples/scripts/denoise_dncnn_universal.py

@@ -0,0 +1,82 @@
+#!/usr/bin/env python
+# -*- coding: utf-8 -*-
+# This file is part of the SCICO package. Details of the copyright
+# and user license can be found in the 'LICENSE.txt' file distributed
+# with the package.
+
+"""
+Comparison of DnCNN Variants for Image Denoising
+================================================
+
+This example demonstrates the solution of an image denoising problem
+using DnCNN :cite:`zhang-2017-dncnn` networks trained for different noise
+levels, as well as custom variants with fewer network layers, and  with a
+noise level input.
+
+The networks trained for specific noise levels are labeled 6L, 6M, 6H,
+17L, 17M, and 17H, where {6, 17} denote the number of layers, and {L, M,
+H} represent noise standard deviation of the training images (0.06, 0.10,
+and 0.20 respectively). The networks with a noise standard deviation
+input are labeled 6N and 17N, where {6, 17} again denote the number of
+layers.
+"""
+
+import numpy as np
+
+import jax
+
+from xdesign import Foam, discrete_phantom
+
+import scico.random
+from scico import metric, plot
+from scico.denoiser import DnCNN
+
+"""
+Create a ground truth image.
+"""
+np.random.seed(1234)
+N = 512  # image size
+x_gt = discrete_phantom(Foam(size_range=[0.075, 0.0025], gap=1e-3, porosity=1), size=N)
+x_gt = jax.device_put(x_gt)  # convert to jax array, push to GPU
+
+"""
+Test different DnCNN variants on images with different noise levels.
+"""
+print("  σ   | variant | noisy image PSNR (dB)   | denoised image PSNR (dB)")
+for σ in [0.06, 0.10, 0.20]:
+    print("------+---------+-------------------------+-------------------------")
+    for variant in ["17L", "17M", "17H", "17N", "6L", "6M", "6H", "6N"]:
+
+        # Instantiate a DnCNN.
+        denoiser = DnCNN(variant=variant)
+
+        # Generate a noisy image.
+        noise, key = scico.random.randn(x_gt.shape, seed=0)
+        y = x_gt + σ * noise
+
+        if variant in ["6N", "17N"]:
+            x_hat = denoiser(y, sigma=σ)
+        else:
+            x_hat = denoiser(y)
+
+        x_hat = np.clip(x_hat, a_min=0, a_max=1.0)
+
+        if variant[0] == "6":
+            variant += " "  # add spaces to maintain alignment
+
+        print(
+            " %.2f | %s     |          %.2f          |          %.2f          "
+            % (σ, variant, metric.psnr(x_gt, y), metric.psnr(x_gt, x_hat))
+        )
+
+
+"""
+Show reference and denoised images for σ=0.2 and variant=6N.
+"""
+fig, ax = plot.subplots(nrows=1, ncols=3, sharex=True, sharey=True, figsize=(21, 7))
+plot.imview(x_gt, title="Reference", fig=fig, ax=ax[0])
+plot.imview(y, title="Noisy image: %.2f (dB)" % metric.psnr(x_gt, y), fig=fig, ax=ax[1])
+plot.imview(x_hat, title="Denoised image: %.2f (dB)" % metric.psnr(x_gt, x_hat), fig=fig, ax=ax[2])
+fig.show()
+
+input("\nWaiting for input to close figures and exit")

examples/scripts/index.rst

@@ -51,6 +51,7 @@ Miscellaneous
    - denoise_tv_pgm.py
    - denoise_tv_multi.py
    - denoise_cplx_tv_pdhg.py
+   - denoise_dncnn_universal.py
    - video_rpca_admm.py
 
 

scico/denoiser.py

@@ -8,7 +8,7 @@
 """Interfaces to standard denoisers."""
 
 
-from typing import Any, Union
+from typing import Any, Optional, Union
 
 import numpy as np
 
@@ -53,8 +53,8 @@ def bm3d(x: JaxArray, sigma: float, is_rgb: bool = False, profile: Union[BM3DPro
         x: Input image. Expected to be a 2D array (gray-scale denoising)
             or 3D array (color denoising). Higher-dimensional arrays are
             tolerated only if the additional dimensions are singletons.
-            For color denoising, the color channel is assumed to be in the
-            last non-singleton dimension.
+            For color denoising, the color channel is assumed to be in
+            the last non-singleton dimension.
         sigma: Noise parameter.
         is_rgb: Flag indicating use of BM3D with a color transform.
             Default: ``False``.
@@ -182,42 +182,75 @@ class DnCNN(FlaxMap):
     Note that :class:`.DnCNNNet` represents an untrained form of the
     generic DnCNN CNN structure, while this class represents a trained
     form with six or seventeen layers.
+
+    The standard DnCNN as proposed in :cite:`zhang-2017-dncnn` does not
+    have a noise level input. This implementation of DnCNN also supports
+    a custom variant that includes a noise standard deviation input,
+    `sigma`, which is included in the network as an additional channel
+    consisting of a constant array with value `sigma`. This network was
+    trained with image data on the range [0, 1], and with noise standard
+    deviations ranging from 0.0 to 0.2. It is worth noting that DRUNet
+    :cite:`zhang-2021-plug`, another recent approach to including a noise
+    level input in a CNN denoiser, is based on a substantially different
+    network architecture.
     """
 
     def __init__(self, variant: str = "6M"):
         """
         Note that all DnCNN models are trained for single-channel image
         input. Multi-channel input is supported via independent denoising
-        of each channel.
+        of each channel. Input images are expected to have pixel values
+        in the range [0, 1].
 
         Args:
             variant: Identify the DnCNN model to be used. Options are
-                '6L', '6M' (default), '6H', '17L', '17M', and '17H',
-                where the integer indicates the number of layers in the
-                network, and the postfix indicates the training noise
-                standard deviation: L (low) = 0.06, M (mid) = 0.1,
-                H (high) = 0.2, where the standard deviations are
-                with respect to data in the range [0, 1].
+                '6L', '6M' (default), '6H', '6N', '17L', '17M', '17H',
+                and '17N', where the integer indicates the number of
+                layers in the network, and the postfix indicates the
+                training noise standard deviation (with respect to data
+                in the range [0, 1]): L (low) = 0.06, M (mid) = 0.10,
+                H (high) = 0.20, or N indicating that a noise standard
+                deviation input, `sigma`, is available.
         """
-        if variant not in ["6L", "6M", "6H", "17L", "17M", "17H"]:
+
+        self.variant = variant
+
+        if variant not in ["6L", "6M", "6H", "17L", "17M", "17H", "6N", "17N"]:
             raise ValueError(f"Invalid value {variant} of parameter variant.")
         if variant[0] == "6":
             nlayer = 6
         else:
             nlayer = 17
-        model = DnCNNNet(depth=nlayer, channels=1, num_filters=64, dtype=np.float32)
+
+        channels = 2 if variant in ["6N", "17N"] else 1
+
+        model = DnCNNNet(depth=nlayer, channels=channels, num_filters=64, dtype=np.float32)
         variables = load_weights(_flax_data_path("dncnn%s.npz" % variant))
         super().__init__(model, variables)
 
-    def __call__(self, x: JaxArray) -> JaxArray:
+    def __call__(self, x: JaxArray, sigma: Optional[float] = None) -> JaxArray:
         r"""Apply DnCNN denoiser.
 
         Args:
             x: Input array.
+            sigma: Noise standard deviation (for variants `6N` and `17N`).
 
         Returns:
             Denoised output.
         """
+        if sigma is not None and self.variant not in ["6N", "17N"]:
+            raise ValueError(
+                "A non-default value for the sigma parameter may "
+                "only be specified when the variant is 6N or 17N"
+                f"; got variant = {self.variant}."
+            )
+
+        if sigma is None and self.variant in ["6N", "17N"]:
+            raise ValueError(
+                "A float value must be specified for the sigma "
+                "parameter when the variant is 6N or 17N."
+            )
+
         if snp.util.is_complex_dtype(x.dtype):
             raise TypeError(f"DnCNN requries real-valued inputs, got {x.dtype}.")
 
@@ -238,13 +271,28 @@ def __call__(self, x: JaxArray) -> JaxArray:
                 )
 
         if x.ndim == 3:
+            y = snp.swapaxes(x, 0, -1)
+
+            if sigma is not None:
+                y = snp.stack([y, snp.ones_like(y) * sigma], -1)
+            else:
+                y = y[..., np.newaxis]
+
             # swap channel axis to batch axis and add singleton axis at end
-            y = super().__call__(snp.swapaxes(x, 0, -1)[..., np.newaxis])
+            y = super().__call__(y)
             # drop singleton axis and swap axes back to original positions
             y = snp.swapaxes(y[..., 0], 0, -1)
+
         else:
+            if sigma is not None:
+                x = snp.stack([x, snp.ones_like(x) * sigma], -1)
+                x = x[np.newaxis, ...]
+
             y = super().__call__(x)
 
+            if sigma is not None:
+                y = y[0, ..., 0]
+
         y = y.reshape(x_in_shape)
 
         return y