Gradient-Guided Residual Learning for Inverse Halftoning and Image Expanding

Image Halftoning and Inverse Halftoning for Optimized Dot Diffusion

Inverse halftoning by decision tree learning

Inverse halftoning is the process to retrieve a (gray) continuous-tone image from a halftone. Recently, machinelearning-based inverse halftoning techniques have been proposed. Decision-tree learning has been applied with success to various machine-learning applications for quite some time. In this paper, we propose to use decision-tree learning to solve the inverse halftoning problem. This allo...

Optimized halftoning using dot diffusion and methods for inverse halftoning

Unlike the error diffusion method, the dot diffusion method for digital halftoning has the advantage of pixel-level parallelism. However, the image quality offered by error diffusion is still regarded as superior to most of the other known methods. We show how the dot diffusion method can be improved by optimization of the so-called class matrix. By taking the human visual characteristics into ...

Image Companding and Inverse Halftoning using Deep Convolutional Neural Networks

This paper presents a deep learning technology for tackling two traditional low-level image processing problems, companding and inverse halftoning. This paper makes two main contributions. First, to the best knowledge of the authors, this is the first work that has successfully developed deep learning based solutions to these two traditional low-level image processing problems. As well as intro...

Inverse halftoning using wavelets

This work introduces a new approach to inverse halftoning using nonorthogonal wavelets. The distinct features of this wavelet-based approach are: 1) edge information in the highpass wavelet images of a halftone image is extracted and used to assist inverse halftoning, 2) cross-scale correlations in the multiscale wavelet decomposition are used for removing background halftoning noise while pres...