The Essential Guide to Image Processing
By A. Bovik, J. E. Adams. J. Electronic Imaging, 2009
615 citations recorded by the source corpus.
Subfields: Electrical and Electronic Engineering, Food Science, Management Information Systems
View Paper: https://www.semanticscholar.org/paper/682c4935b0e7fcbaa24c8d9a9545c1178675aa9e
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Citations in the corpus
- Using Deep Neural Networks for Inverse Problems in Imaging: Beyond Analytical Methods (2018, background): “A more detailed description of the inverse problems mentioned here can be found in [1]–[3].”
- Implementation and Benchmarking of Perceptual Image Hash Functions (2010, methodology): “The Laplacian estimate for an image, f(n1, n2), is then ∇̂(2)f(n1, n2) = f(n1, n2) ∗ h(n1, n2), ([4], 3.”
- PSNR vs SSIM: imperceptibility quality assessment for image steganography (2020, methodology): “Another way of processing RGB images is by converting RGB images to separate intensity components from color components, then intensity components can be processed using different algorithms with image components, after all, components can be recombined after processing [9, 46].”