Distributed under Creative Commons Cc-by 4.0 Scikit-image: Image Processing in Python

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Array programming with NumPy (2020, background): “2) builds on top of this foundation to provide several, widely used technique specific libraries [16, 17, 23], that in turn underlay numerous domain specific projects [24, 25, 26, 27, 28, 29].”
Generalisation in humans and deep neural networks (2018, methodology): “For the experiments using greyscale images the stimuli were converted using the rgb2gray method [73] in Python.”
Lossy Image Compression with Compressive Autoencoders (2017, methodology): “We used the implementation of van der Walt et al. (2014) for SSIM and the implementation of Toderici et al. (2016b) for MS-SSIM.”
Noise2Self: Blind Denoising by Self-Supervision (2019, methodology): “The image denoising methods were all demonstrated on the full camera image included in the scikit-image library for python (van der Walt et al., 2014).”
DIOPTAS: a program for reduction of two-dimensional X-ray diffraction data and data exploration (2015, methodology): “Numerical operations and image processing are speeded up by the NumPy, SciPy [7] and Scikit-image [8] packages.”