An implementation of the BM3D(sparse 3D transform-domain collaborative filtering) denoising algorithm for Julia.
Within Julia, use the Pkg:
using Pkg
Pkg.add("BM3DDenoise")using BM3DDenoise
using Images
img = load("noise_image_path")
denoise_img = bm3d(img, noise_variance) # noise_variance noise_variance is the variance of the noise.using BM3DDenoise
using Images
using Downloads
noise_image_path = download("http://www.cs.tut.fi/~foi/GCF-BM3D/images/Lena512_noi_s10.png")
noise_variance = 10 / 255
img = load(noise_image_path)
denoise_img = bm3d(img, noise_variance) # noise_variance noise_variance is the variance of the noise.If you want to customize parameters, see:
?bm3d_configFor the image that does not know the noise variance, we can use Wavelets.jl to make noise estimation.
using Pkg; Pkg.add("Wavelets")
using Wavelets
using Images
noise_img = load("image_path")
# Convert to YCrCb color space for estimation
noise_img = YCbCr.(noise_img)
noise_img_y = channelview(img_y)[1,:,:] # Extract y channel.
# normalization
noise_img_y .-= 16.
noise_img_y ./= (235. - 16.)
noise_variance = Threshold.noisest(noise_img_y)Thank you for your help in this project!
https://webpages.tuni.fi/foi/GCF-BM3D/
https://github.com/rcrandall/BM3D.jl
https://blog.csdn.net/qq_33552519/article/details/108632146
http://www.ipol.im/pub/art/2012/l-bm3d/article.pdf
https://blog.csdn.net/github_28260175/article/details/81101457
https://blog.csdn.net/qq_36955294/article/details/83443317