168 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
Watermarking-Based Inpainting Under Data Transmition Environment
[[abstract]]This studyb proposes a novel image inpainting technique based on watermarking and halftoning. This technique use LSB method to embed error diffusion halftone image into
original image for protecting the image. In image repair process, we use LSB method to extract the halftone information, and the reference image is achieved from LUT inverse halftone. Finally we use the reference imageto finish the image inpainting work. Experiment shows the performance of our method is very excellent in image inpainting.[[conferencetype]]國際[[conferencedate]]20101206~20101208[[booktype]]電子版[[iscallforpapers]]Y[[conferencelocation]]Chengdu, Chin
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
New methods for digital halftoning and inverse halftoning
Halftoning is the rendition of continuous-tone pictures on bi-level displays. Here we first review some of the halftoning algorithms which have a direct bearing on our paper and then describe some of the more recent advances in the field. Dot diffusion halftoning has the advantage of pixel-level parallelism, unlike the popular error diffusion halftoning method. We first review the dot diffusion algorithm and describe a recent method to improve its image quality by taking advantage of the Human Visual System function. Then we discuss the inverse halftoning problem: The reconstruction of a continuous tone image from its halftone. We briefly review the methods for inverse halftoning, and discuss the advantages of a recent algorithm, namely, the Look Up Table (LUT)Method. This method is extremely fast and achieves image quality comparable to that of the best known methods. It can be applied to any halftoning scheme. We then introduce LUT based halftoning and tree-structured LUT (TLUT)halftoning. We demonstrate how halftone image quality in between that of error diffusion and Direct Binary Search (DBS)can be achieved depending on the size of tree structure in TLUT algorithm while keeping the complexity of the algorithm much lower than that of DBS
Task-Driven Dictionary Learning
Modeling data with linear combinations of a few elements from a learned
dictionary has been the focus of much recent research in machine learning,
neuroscience and signal processing. For signals such as natural images that
admit such sparse representations, it is now well established that these models
are well suited to restoration tasks. In this context, learning the dictionary
amounts to solving a large-scale matrix factorization problem, which can be
done efficiently with classical optimization tools. The same approach has also
been used for learning features from data for other purposes, e.g., image
classification, but tuning the dictionary in a supervised way for these tasks
has proven to be more difficult. In this paper, we present a general
formulation for supervised dictionary learning adapted to a wide variety of
tasks, and present an efficient algorithm for solving the corresponding
optimization problem. Experiments on handwritten digit classification, digital
art identification, nonlinear inverse image problems, and compressed sensing
demonstrate that our approach is effective in large-scale settings, and is well
suited to supervised and semi-supervised classification, as well as regression
tasks for data that admit sparse representations.Comment: final draft post-refereein
P-HIP: A Multiresolution halftoning algorithm for progressive display
This thesis describes and implements an algorithmic framework for memory efficient, \u27on-the-fly\u27 halftoning in a progressive transmission environment. Instead of a conventional approach which repeatedly reconstructs the continuous tone image from memory and subsequently halftones it for display, the proposed method achieves significant memory efficiency by storing only the halftoned image and updating it in response to additional information received through progressive transmission. Thus the method requires only a single frame-buffer of bits for storage of the displayed binary image and no additional storage is required for the contone data. The additional image data received through progressive transmission is accommodated through in-place updates of the buffer. The method is thus particularly advantageous for high resolution bi-level displays where it can result in significant savings in memory. The proposed framework is implemented using a suitable multi-resolution, multi-level modification of error diffusion that is motivated by the presence of a single binary frame-buffer. Aggregates of individual display bits constitute the multiple output levels at a given resolution. This creates a natural progression of increasing resolution with decreasing bit-depth. Output images are shown to be comparable in terms of quality to those obtained from the conventional Floyd Steinberg error diffusion algorithm
Optimising Spatial and Tonal Data for PDE-based Inpainting
Some recent methods for lossy signal and image compression store only a few
selected pixels and fill in the missing structures by inpainting with a partial
differential equation (PDE). Suitable operators include the Laplacian, the
biharmonic operator, and edge-enhancing anisotropic diffusion (EED). The
quality of such approaches depends substantially on the selection of the data
that is kept. Optimising this data in the domain and codomain gives rise to
challenging mathematical problems that shall be addressed in our work.
In the 1D case, we prove results that provide insights into the difficulty of
this problem, and we give evidence that a splitting into spatial and tonal
(i.e. function value) optimisation does hardly deteriorate the results. In the
2D setting, we present generic algorithms that achieve a high reconstruction
quality even if the specified data is very sparse. To optimise the spatial
data, we use a probabilistic sparsification, followed by a nonlocal pixel
exchange that avoids getting trapped in bad local optima. After this spatial
optimisation we perform a tonal optimisation that modifies the function values
in order to reduce the global reconstruction error. For homogeneous diffusion
inpainting, this comes down to a least squares problem for which we prove that
it has a unique solution. We demonstrate that it can be found efficiently with
a gradient descent approach that is accelerated with fast explicit diffusion
(FED) cycles. Our framework allows to specify the desired density of the
inpainting mask a priori. Moreover, is more generic than other data
optimisation approaches for the sparse inpainting problem, since it can also be
extended to nonlinear inpainting operators such as EED. This is exploited to
achieve reconstructions with state-of-the-art quality.
We also give an extensive literature survey on PDE-based image compression
methods
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