Digital Color Halftoning
A Non-Local Algorithm for Image Denoising
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Deblurring Images: Matrices, Spectra, and Filtering (Fundamentals of Algorithms 3) (Fundamentals of Algorithms)
Steganalysis of halftone image using inverse halftoning
Signal Processing
Iterated conditional modes for inverse dithering
Signal Processing
A New Alternating Minimization Algorithm for Total Variation Image Reconstruction
SIAM Journal on Imaging Sciences
Adaptive energy diffusion for blind inverse halftoning
PCM'10 Proceedings of the 11th Pacific Rim conference on Advances in multimedia information processing: Part I
Inverse halftoning via MAP estimation
IEEE Transactions on Image Processing
A fast, high-quality inverse halftoning algorithm for error diffused halftones
IEEE Transactions on Image Processing
Look-up table (LUT) method for inverse halftoning
IEEE Transactions on Image Processing
Inverse halftoning algorithm using edge-based lookup table approach
IEEE Transactions on Image Processing
Color recovery of black-and-white halftoned images via categorized color-embedding look-up tables
Digital Signal Processing
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The quality of the reconstructed image with 255 discrete levels from its halftoned version of homogenously distributed dot patterns depends on how well the fine textures can be represented and how the noisy dot patterns can be simultaneously removed on the flat regions. To satisfy these criteria, an iterative inverse halftoning method based on the texture-enhancing deconvolution with error-compensating feedback is presented. In this study, the input halftoned image is initially low-pass filtered with the Gaussian filtering to generate a blurred image on which the textures or details are sharply restored and the noisy halftoned dots on the flat regions are forced to be suppressed through the joint of the texture-enhancing deconvolution with spatially varying image priors and the structure-preserving image denoising. Moreover, the initially blurred image is iteratively updated with the addition of the created error image defined as the difference-image between the low-pass-filtered input halftoned image and the low-pass filtered halftoned image of the reconstructed continuous image, thereby compensating the missing textures. This error compensation is conducted until the stop criterion is satisfied. The experiment results showed that the proposed method not only reproduced the fine textures or details but also suppressed the noisy dots on the flat regions, more than the conventional state-of-the-art methods.