Add BM3D(sparse 3D transform-domain collaborative filtering) denoising algorithm.

Project.toml

@@ -8,6 +8,7 @@ Distributions = "31c24e10-a181-5473-b8eb-7969acd0382f"
 ImageCore = "a09fc81d-aa75-5fe9-8630-4744c3626534"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
+UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
 [compat]
 Distributions = "0.19, 0.20, 0.21, 0.22, 0.23, 0.24, 0.25"
@@ -16,6 +17,7 @@ Reexport = "0.2, 1.0"
 julia = "1.6"
 
 [extras]
+BM3DDenoise = "95fb3b36-088a-43fb-bb1b-b1f34fadbd7d"
 ImageIO = "82e4d734-157c-48bb-816b-45c225c6df19"
 ImageMagick = "6218d12a-5da1-5696-b52f-db25d2ecc6d1"
 ImageQualityIndexes = "2996bd0c-7a13-11e9-2da2-2f5ce47296a9"
@@ -25,4 +27,4 @@ Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 TestImages = "5e47fb64-e119-507b-a336-dd2b206d9990"
 
 [targets]
-test = ["ImageIO", "ImageMagick", "ImageQualityIndexes", "ImageTransformations", "ReferenceTests", "Test", "TestImages"]
+test = ["BM3DDenoise", "ImageIO", "ImageMagick", "ImageQualityIndexes", "ImageTransformations", "ReferenceTests", "Test", "TestImages"]

docs/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+BM3DDenoise = "95fb3b36-088a-43fb-bb1b-b1f34fadbd7d"
 DemoCards = "311a05b2-6137-4a5a-b473-18580a3d38b5"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"

docs/examples/reduce_noise/BM3D.jl

@@ -0,0 +1,31 @@
+# title: The BM3D(sparse 3D transform-domain collaborative filtering) denoising algorithm.
+# id: bm3d-demo
+# cover: assets/bm3d_cover.png
+# date: 2021-08-04
+# author: Longhao Chen <Longhao.Chen@outlook.com>
+# description: This demo shows how to use the BM3D denoising algorithm to reduce gaussian noise
+# ---
+
+using ImageNoise
+using TestImages, ImageShow, ImageCore, ImageQualityIndexes, ImageTransformations
+using FileIO, Random #src
+
+# First, load an image and and add gaussian noise to it
+
+gray_img = float.(imresize(testimage("cameraman"), ratio=0.5))
+n = AdditiveWhiteGaussianNoise(0.1)
+noisy_img = apply_noise(gray_img, n)
+
+# Then calling the standard `reduce_noise` API
+
+f_bm3d = BM3D(0.1)
+denoised_img = reduce_noise(noisy_img, f_bm3d)
+
+mosaicview(gray_img, noisy_img, denoised_img; nrow=1)
+
+# Get the PSNR using ImageQualityIndexes package:
+
+assess_psnr(gray_img, denoised_img)
+
+mkpath("assets") #src
+save("assets/bm3d_cover.png", denoised_img) #src

src/ReduceNoise/BM3DDenoise.jl

@@ -0,0 +1,65 @@
+using UUIDs
+const BM3DDenoise = Base.PkgId(UUID("95fb3b36-088a-43fb-bb1b-b1f34fadbd7d"), "BM3DDenoise")
+
+# Rewrite from ImageIO.jl
+function checked_import(pkgid)
+    Base.root_module_exists(pkgid) && return Base.root_module(pkgid)
+    # If not available, load the library or throw an error.
+    Base.require(pkgid)
+end
+
+
+@doc raw"""
+    BM3D(σ [, config=bm3d_config()])
+
+The BM3D(sparse 3D transform-domain collaborative filtering) denoising algorithm.
+
+!!! info
+
+    This feature depends on the package: `BM3DDenoise`
+
+    You can install it by `Pkg`:
+    ```
+    using Pkg
+    Pkg.add("BM3DDenoise")
+    ```
+
+# Arguments
+
+* `σ::Float64` is the variance of the noise.
+
+* `config::bm3d_config` is `BM3DDenoise.bm3d_config`.
+
+# Examples
+
+```julia
+img = testimage("lena_color_256")
+
+n = AdditiveWhiteGaussianNoise(0.1)
+noisy_img = apply_noise(img, n)
+
+# use default arguments
+f_denoise = BM3D(0.1)
+denoised_img = reduce_noise(noisy_img, f_denoise)
+```
+
+See also: [`reduce_noise`](@ref), [`reduce_noise!`](@ref)
+"""
+struct BM3D <: AbstractImageDenoiseAlgorithm
+    """degree of filtering"""
+    σ::Float64
+    """bm3d_config"""
+    config
+    function BM3D(σ, config)
+        σ > 0 || @warn "σ is supposed to be positive"
+        new(σ, config)
+    end
+end
+BM3D(σ) = BM3D(σ, Base.invokelatest(checked_import(BM3DDenoise).bm3d_config))
+
+function (f::BM3D)(out::AbstractArray{T},
+                           img::AbstractArray) where T
+    axes(out) == axes(img) || ArgumentError("Images should have the same axes.")
+    out .= T.(Base.invokelatest(checked_import(BM3DDenoise).bm3d, img, f.σ, f.config))
+    return out
+end

src/ReduceNoise/ReduceNoise.jl

@@ -9,11 +9,14 @@ using ImageCore: NumberLike, GenericGrayImage, GenericImage
 import ..NoiseAPI: AbstractImageDenoiseAlgorithm, reduce_noise, reduce_noise!
 
 include("compat.jl")
+include("BM3DDenoise.jl")
 include("NonlocalMean.jl")
 
 export
     reduce_noise, reduce_noise!,
 
+    # BM3D
+    BM3D,
     # Non-local mean filter for gaussian noise
     NonlocalMean, get_NonlocalMean_rp
 

test/ReduceNoise/BM3DDenoise.jl

@@ -0,0 +1,14 @@
+@testset "BM3DDenoise" begin
+    @info "Test: BM3DDenoise"
+    @testset "Numeric" begin
+        img_gray = n0f8.(imresize(testimage("lena_gray_256"); ratio=0.25))
+        n = AdditiveWhiteGaussianNoise(0.05)
+        noisy_img = apply_noise(img_gray, n; rng=MersenneTwister(0))
+
+        f = BM3D(0.05)
+        denoised_img = reduce_noise(noisy_img, f)
+        # further modification shall not decrease psnr and ssim
+        @test assess(PSNR(), denoised_img, img_gray) >= 28.
+        @test assess(SSIM(), denoised_img, img_gray) >= 0.9
+    end
+end

test/runtests.jl

@@ -12,6 +12,7 @@ include("ApplyNoise/AdditiveWhiteGaussianNoise.jl")
 # ReduceNoise
 @info "Test: ReduceNoise"
 include("ReduceNoise/NonlocalMean.jl")
+include("ReduceNoise/BM3DDenoise.jl")
 end
 
 nothing