using Images, TestImages using Random img_orig = float64.(testimage("cameraman")) assess_ssim(img_orig, img_orig) noise = ones(size(img_orig)) .* 0.2 .* (maximum(img_orig) - minimum(img_orig)) img_const = img_orig + noise mask = rand(Float64, size(img_orig)) .< 0.5 noise[mask] = noise[mask] .* -1 img_noise = img_orig + noise mosaicview(img_const, img_noise; nrow=1) mse(img_orig, img_const), mse(img_orig, img_noise) assess_ssim(img_orig, img_const), assess_ssim(img_orig, img_noise) iqi = SSIM(KernelFactors.gaussian(2.0, 11), (0.5, 0.5, 0.5)) assess(iqi, img_orig, img_const) peakval = maximum(img_orig) .|> Float64 # peakval is max pixel value in original image assess_psnr(img_noise, img_orig, [peakval]) # 13.979400086720483 noise = ones(size(img_orig)) .* 0.2 .* (maximum(img_orig) - minimum(img_orig)) mask = rand(Float64, size(img_orig)) .< 0.5 noise[mask] = noise[mask] .* -3 img_noise = img_orig + noise assess_psnr(img_noise, img_orig) # This file was generated using Literate.jl, https://github.com/fredrikekre/Literate.jl