98 research outputs found
Novel methods in image halftoning
Ankara : Department of Electrical and Electronics Engineering and Institute of Engineering and Science, Bilkent Univ., 1998.Thesis (Master's) -- Bilkent University, 1998.Includes bibliographical references leaves 97-101Halftoning refers to the problem of rendering continuous-tone (contone) images on display and printing devices which are capable of reproducing only a limited number of colors. A new adaptive halftoning method using the adaptive QR- RLS algorithm is developed for error diffusion which is one of the halftoning techniques. Also, a diagonal scanning strategy to exploit the human visual system properties in processing the image is proposed. Simulation results on color images demonstrate the superior quality of the new method compared to the existing methods. Another problem studied in this thesis is inverse halftoning which is the problem of recovering a contone image from a given halftoned image. A novel inverse halftoning method is developed for restoring a contone image from the halftoned image. A set theoretic formulation is used where sets are defined using the prior information about the problem. A new space domain projection is introduced assuming the halftoning is performed ,with error diffusion, and the error diffusion filter kernel is known. The space domain, frequency domain, and space-scale domain projections are used alternately to obtain a feasible solution for the inverse halftoning problem which does not have a unique solution. Simulation results for both grayscale and color images give good results, and demonstrate the effectiveness of the proposed inverse halftoning method.Bozkurt, GözdeM.S
Restoration of error-diffused images using projection onto convex sets
Cataloged from PDF version of article.In this paper, a novel inverse halftoning method is
proposed to restore a continuous tone image from a given half-tone
image. A set theoretic formulation is used where three sets are defined
using the prior information about the problem. A new spacedomain
projection is introduced assuming the halftoning is performed
using error diffusion, and the error diffusion filter kernel is
known. The space-domain, frequency-domain, and space-scale domain
projections are used alternately to obtain a feasible solution
for the inverse halftoning problem which does not have a unique
solution
A POCS-based restoration algorithm for restoring halftoned color-quantized images
Centre for Multimedia Signal Processing, Department of Electronic and Information Engineering2006-2007 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe
Taming Reversible Halftoning via Predictive Luminance
Traditional halftoning usually drops colors when dithering images with binary
dots, which makes it difficult to recover the original color information. We
proposed a novel halftoning technique that converts a color image into a binary
halftone with full restorability to its original version. Our novel base
halftoning technique consists of two convolutional neural networks (CNNs) to
produce the reversible halftone patterns, and a noise incentive block (NIB) to
mitigate the flatness degradation issue of CNNs. Furthermore, to tackle the
conflicts between the blue-noise quality and restoration accuracy in our novel
base method, we proposed a predictor-embedded approach to offload predictable
information from the network, which in our case is the luminance information
resembling from the halftone pattern. Such an approach allows the network to
gain more flexibility to produce halftones with better blue-noise quality
without compromising the restoration quality. Detailed studies on the
multiple-stage training method and loss weightings have been conducted. We have
compared our predictor-embedded method and our novel method regarding spectrum
analysis on halftone, halftone accuracy, restoration accuracy, and the data
embedding studies. Our entropy evaluation evidences our halftone contains less
encoding information than our novel base method. The experiments show our
predictor-embedded method gains more flexibility to improve the blue-noise
quality of halftones and maintains a comparable restoration quality with a
higher tolerance for disturbances.Comment: to be published in IEEE Transactions on Visualization and Computer
Graphic
Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning
Layer decomposition to separate an input image into base and detail layers
has been steadily used for image restoration. Existing residual networks based
on an additive model require residual layers with a small output range for fast
convergence and visual quality improvement. However, in inverse halftoning,
homogenous dot patterns hinder a small output range from the residual layers.
Therefore, a new layer decomposition network based on the Gaussian convolution
model (GCM) and structure-aware deblurring strategy is presented to achieve
residual learning for both the base and detail layers. For the base layer, a
new GCM-based residual subnetwork is presented. The GCM utilizes a statistical
distribution, in which the image difference between a blurred continuous-tone
image and a blurred halftoned image with a Gaussian filter can result in a
narrow output range. Subsequently, the GCM-based residual subnetwork uses a
Gaussian-filtered halftoned image as input and outputs the image difference as
residual, thereby generating the base layer, i.e., the Gaussian-blurred
continuous-tone image. For the detail layer, a new structure-aware residual
deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of
the base layer, the SARDS uses the predicted base layer as input and outputs
the deblurred version. To more effectively restore image structures such as
lines and texts, a new image structure map predictor is incorporated into the
deblurring network to induce structure-adaptive learning. This paper provides a
method to realize the residual learning of both the base and detail layers
based on the GCM and SARDS. In addition, it is verified that the proposed
method surpasses state-of-the-art methods based on U-Net, direct deblurring
networks, and progressively residual networks
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