341 research outputs found
Backward Diffusion Methods for Digital Halftoning
We examine using discrete backward diffusion to produce digital halftones.
The noise introduced by the discrete approximation to backwards diffusion forces
the intensity away from uniform values, so that rounding each pixel to black or
white can produce a pleasing halftone. We formulate our method by considering
the Human Visual System norm and approximating the inverse of the blurring
operator. We also investigate several possible mobility functions for use in a
nonlinear backward diffusion equation for higher quality results
Digital Color Imaging
This paper surveys current technology and research in the area of digital
color imaging. In order to establish the background and lay down terminology,
fundamental concepts of color perception and measurement are first presented
us-ing vector-space notation and terminology. Present-day color recording and
reproduction systems are reviewed along with the common mathematical models
used for representing these devices. Algorithms for processing color images for
display and communication are surveyed, and a forecast of research trends is
attempted. An extensive bibliography is provided
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
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
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
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