IMDSP-4.1

MLP Interpolation for Digital Image Processing Using Wavelet Transform
Yu-Len Huang, Ruey-Feng Chang (Department of Computer Science and Information Engineering, National Chung Cheng University, Taiwan, R.O.C.)

In this paper, we present nonlinear interpolation schemes for image resolution enhancement. The Multilayer perceptron (MLP) interpolation schemes based on the wavelet transform and subband filtering are proposed. Because estimating each sub-image signal is more effective than estimating the whole image signal, pixels in the low-resolution image are used as input signal of the MLP to estimate all of the wavelet sub-image of the corresponding high-resolution image. The image of increased resolution is finally produced by the synthesis procedure of wavelet transform. As compared with other popular methods, the results show that the improvement is remarkable. The detail simulation results of interpolated images and image sequences can be found in web page: http://www.cs.ccu.edu.tw/~hyl/wmi/.

IMDSP-4.2

Contrast Invariant Registration of Images
Pascal Monasse (CMLA, ENS Cachan)

We propose a method for image registration which seems to be useful under the three following conditions. First, both images are globally and roughly the result of a translation and rotation. Second, some occlusions due to moving objects occur from image 1 to image 2. Third, because of changes of illumination, contrast may have changed globally and even locally. Under such unfavorable conditions, correlation-based global registration may become inaccurate, because of the global compromise it yields between several displacements. Our method avoids these difficulties by defining a set of local contrast invariant features in order to achieve contrast invariant matching. A voting procedure allows to eliminate "wrong" matching features due to the displacement of small objects and yields sub-pixel accuracy. This method was tested successfully for registration of watches with moving hands and for road control applications.

IMDSP-4.3

Restoration of Error-Diffused Images Using POCS
Gozde Bozkurt (North Carolina State University, Electrical and Computer Engineering Department), A. Enis Cetin (Bilkent University, Electrical Engineering Department)

Halftoning is a process that deliberately injects noise into the original image in order to obtain visually pleasing output images with a smaller number of bits per pixel for displaying or printing purposes. In this paper, a novel inverse halftoning method is proposed to restore a continuous tone image from the given halftone image. A set theoretic formulation is used where three 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.

IMDSP-4.4

Multi-Channel High Resolution Blind Image Restoration
- Wirawan, Pierre Duhamel, Henri Maitre (Dept. Traitement du Signal et des Images, ENST Paris)

We address the reconstruction problem of a high resolution image from its undersampled measurements accross multiple FIR channels with unknown response. Our method consists of two stages: blind multi-input-multi-output (MIMO) deconvolution using FIR filters and blind separation of mixed polyphase components. The proposed deconvolution method is based in the mutually referenced equalizers (MRE) algorithm previously developed for blind equalization in digital communications. For source separation, a method is proposed for separating mixed polyphase components of a bandlimited signal. The existing blind sources separation algorithms assume that the source signals are either independent or uncorrelated, which is not the case when the sources are polyphase components of a bandlimited signal. Simulation results on artificial and photographics images are given.

IMDSP-4.5

A Comparative Study Between Parametric Blur Estimation Methods
Sophie CHARDON (Diagnostic Radiology Department-M.D. Anderson Cancer Center, Houston), Benoit VOZEL, Kacem CHEHDI (LASTI-ENSSAT-Universite de RENNES 1)

In pattern recognition problems, the effectiveness of the analysis depends heavily on the quality of the image to be processed. This image may be blurred and/or noisy and the goal of digital image restoration is to find an estimate of the original image. A fundamental issue in this process is the blur estimation. When the blur is not readily avalaible, it has to be estimated from the observed image. Two main approaches can be found in the literature. The first one identify the blur parameters before any restoration whereas the second one realizes these two steps jointly. We present a comparative study of several parametric blur estimation methods, based on a parametric ARMA modeling of the image, belonging to the first approach. Our purpose is to evaluate the acuracy of the various methods, on which the restoration procedure relies, and their robustness to modeling assumptions, noise, and size of support.

IMDSP-4.6

Image Reconstruction with Two-Dimensional Piecewise Polynomial Convolution
Stephen E Reichenbach, Frank Geng (Department of Computer Science and Engineering, University of Nebraska-Lincoln)

This paper describes two-dimensional, non-separable piecewise polynomial convolution for image reconstruction. We investigate a two-parameter kernel with support [-2,2]x[-2,2] and constrained for smooth reconstruction. Performance reconstructing a sampled random Markov field is superior to the traditional one-dimensional cubic convolution algorithm.

IMDSP-4.7

Wavelet-Based Deconvolution for Ill-Conditioned Systems
Ramesh Neelamani, Hyeokho Choi, Richard G Baraniuk (Department of Electrical and Computer Engineering, Rice University, Houston, TX 77251-1892, USA)

In this paper, we propose a new approach to wavelet-based deconvolution. Roughly speaking, the algorithm comprises Fourier-domain system inversion followed by wavelet-domain noise suppression. Our approach subsumes a number of other wavelet-based deconvolution methods. In contrast to other wavelet-based approaches, however, we employ a regularized inverse filter, which allows the algorithm to operate even when the inverse system is ill-conditioned or non-invertible. Using a mean-square-error metric, we strike an optimal balance between Fourier-domain and wavelet-domain regularization. The result is a fast deconvolution algorithm ideally suited to signals and images with edges and other singularities. In simulations with real data, the algorithm outperforms the LTI Wiener filter and other wavelet-based deconvolution algorithms in terms of both visual quality and MSE performance.

IMDSP-4.8

2-D BINARY LOCALLY MONOTONIC REGRESSION
Alfredo Restrepo (Universidad de los Andes), Scott T Acton (Oklahoma State University)

We introduce binary locally monotonic regression as a first step in the study of the application of local monotonicity for image estimation. Given an algorithm that generates a similar locally monotonic image from a given image, we can specify both the scale of the image features retained and the image smoothness. In contrast to the median filter and to morphological filters, a locally monotonic regression produces the optimally similar locally monotonic image. Locally monotonic regression is a computationally expensive technique, and the restriction to binary-range signals allows the use of Viterbi-type algorithms. Binary locally monotonic regression is a powerful tool that can be used in the solution of the image estimation, image enhancement, and image segmentation problems.

IMDSP-4.9

Preconditioners for regularized image superresolution
Nhat Nguyen (Stanford University), Peyman Milanfar (SRI International), Gene Golub (Stanford University)

Superresolution reconstruction produces a high resolution image from a set of low resolution images. Previous work had not adequately addressed the computational issues for this problem. In this paper, we propose efficient block circulant preconditioners for solving the regularized superresolution problem by CG. Effectiveness of our preconditioners is demonstrated with superresolution results for a simulated image sequence and a FLIR image sequence.

IMDSP-4.10

Spatially Adaptive Statistical Modeling of Wavelet Image Coefficients and Its Application to Denoising
Mehmet K Mihcak, Igor Kozintsev, Kannan Ramchandran (University of Illinois, Urbana-Champaign)

This paper deals with the application to denoising of a very simple but effective "local" spatially adaptive statistical model for the wavelet image representation that was recently introduced successfully in a compression context. Motivated by the intimate connection between compression and denoising, this paper explores the significant role of the underlying statistical wavelet image model. The model used here, a simplified version of the one in , is that of a {\em mixture process} of $independent$ component fields having a zero-mean Gaussian distribution with unknown variances $\sigma(k)^2$ that are slowly {\em spatially-varying} with the wavelet coefficient location $k$. We propose to use this model for image denoising by initially estimating the underlying variance field using a Maximum Likelihood (ML) rule and then applying the Minimum Mean Squared error (MMSE) estimation procedure. In the process of variance estimation, we assume that the variance field is ``locally'' smooth to allow its reliable estimation, and use an adaptive window-based estimation procedure to capture the effect of edges. Our denoising results compare favorably with the best reported results in the recent denoising literature.

IMDSP-4.11

Bayesian Image Restoration Using a Wavelet-Based Subband Decomposition
Rafael Molina (Universidad de Granada), Aggelos K Katsaggelos (Northwestern University), Javier Abad (Universidad de Granada)

In this paper the subband decomposition of a single channel image restoration problem is examined. The decomposition is carried out in the image model (prior model) in order to take into account the frequency activity of each band of the original image. The hyperparameters associated with each band together with the original image are rigorously estimated within the Bayesian framework. Finally, the proposed method is tested and compared with other methods on real images.

IMDSP-4.12

An Optimal Set-Theoretic Blind Deconvolution Scheme Based on Hybrid Steepest Descent Method
Isao Yamada, Masanori Kato, Kohichi Sakaniwa (Dept. of E & E Eng., Tokyo Inst. of Tech.)

In this paper, we propose a simple set-theoretic blind deconvolution scheme based on a recently developed convex projection technique called Hybrid Steepest Descent Methods. The scheme is essentially motivated by Kundur and Hatzinakos' idea that minimizes a certain cost function uniformly reflecting all a priori informations such that (i) nonnegativity of the true image and (ii) support size of the original object. The most remarkable feature of the proposed scheme is that the proposed one can utilize each a priori information separately from other ones, where some partial informations are treated in a set-theoretic sense while the others are incorporated in a cost function to be minimized.

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Last Update: February 4, 1999 Ingo Höntsch