Jan Biemond

Iterative methods for image deblurring

The authors discuss the use of iterative restoration algorithms for the removal of linear blurs from photographic images that may also be assumed to be degraded by pointwise nonlinearities such as film saturation and additive noise. Iterative algorithms allow for the incorporation of various types of prior knowledge about the class of feasible solutions, can be used to remove nonstationary blurs, and are fairly robust with respect to errors in the approximation of the blurring operator. Special attention is given to the problem of convergence of the algorithms, and classical solutions such as inverse filters, Wiener filters, and constrained least-squares filters are shown to be limiting solutions of variations of the iterations. Regularization is introduced as a means for preventing the excessive noise magnification that is typically associated with ill-conditioned inverse problems such as the deblurring problem, and it is shown that noise effects can be minimized by terminating the algorithms after a finite number of iterations. The role and choice of constraints on the class of feasible solutions are also discussed. >

A regularized iterative image restoration algorithm

The development of the algorithm is based on a set theoretic approach to regularization. Deterministic and/or statistical information about the undistorted image and statistical information about the noise are directly incorporated into the iterative procedure. The restored image is the center of an ellipsoid bounding the intersection of two ellipsoids. The proposed algorithm, which has the constrained least squares algorithm as a special case, is extended into an adaptive iterative restoration algorithm. The spatial adaptivity is introduced to incorporate properties of the human visual system. Convergence of the proposed iterative algorithms is established. For the experimental results which are shown, the adaptively restored images have better quality than the nonadaptively restored ones based on visual observations and on an objective criterion of merit which accounts for the noise masking property of the visual system. >

Automated high-level movie segmentation for advanced video-retrieval systems

We present a newly developed strategy for automatically segmenting movies into logical story units. A logical story unit can be understood as an approximation of a movie episode, which is a high-level temporal movie segment, characterized either by a single event (dialog, action scene, etc.) or by several events taking place in parallel. Since we consider a whole event and not a single shot to be the most natural retrieval unit for the movie category of video programs, the proposed segmentation is the crucial first step toward a concise and comprehensive content-based movie representation for browsing and retrieval purposes. The automation aspect is becoming increasingly important with the rising amount of information to be processed in video archives of the future. The segmentation process is designed to work on MPEG-DC sequences, where we have taken into account that at least a partial decoding is required for performing content-based operations on MPEG compressed video streams. The proposed technique allows for carrying out the segmentation procedure in a single pass through a video sequence.

Regularized iterative image restoration with ringing reduction

Linear space-invariant image restoration algorithms often introduce ringing effects near sharp intensity transitions. It is shown that these artifacts are attributable to the regularization of the ill-posed image restoration problem. Two possible methods to reduce the ringing effects in restored images are proposed. The first method incorporates deterministic a priori knowledge about the original image into the restoration algorithm. The second method locally regulates the severity of the noise magnification and the ringing phenomenon, depending on the edge information in the image. A regularized iterative image restoration algorithm is proposed in which both ringing reduction methods are included by making use of the theory of the projections onto convex sets and the concept of norms in a weighted Hilbert space. Both the numerical performance and the visual evaluation of the results are improved by the use of ringing reduction. >

Subband coding of images using vector quantization

A novel two-dimensional subband coding technique is presented that can be applied to images as well as speech. A frequency-band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 equal-rate subbands. These 16 parallel subband signals are regarded as a 16-dimensional vector source and coded as such using vector quantization. In the asymptotic case of high bit rates, a theoretical analysis yields that a lower bound to the gain is attainable by choosing this approach over scalar quantization of each subband with an optimal bit allocation. It is shown that vector quantization in this scheme has several advantages over coding the subbands separately. Experimental results are given, and it is shown the scheme has a performance that is comparable to that of more complex coding techniques. >

Maximum likelihood image and blur identification: a unifying approach

A number of different algorithms have recently been proposed to identify the image and blur model parameters from an image that is

A pel-recursive Wiener-based displacement estimation algorithm

Abstract In this paper, a pel-recursive Wiener-based displacement estimation algorithm is introduced. This algorithm is based on the assumption that both the so-called update and the linearization error are samples of stochastic processes. It provides a linear least-squares estimate of the update using N observations to obtain a reliable displacement estimate, and it has proven to be very successful to compensate motion in some typical video conferencing scenes. A comparison of the Wiener-based algorithm with some other well-known pel-recursive techniques shows the favourable behaviour of the Wiener algorithm with respect to robustness, stability, and convergence.

A fast Kalman filter for images degraded by both blur and noise

In this paper a fast Kalman filter is derived for the nearly optimal recursive restoration of images degraded in a deterministic way by blur and in a stochastic way by additive white noise. Straightforwardly implemented optimal restoration schemes for two-dimensional images degraded by both blur and noise create dimensionality problems which, in turn, lead to large storage and computational requirements. When the band-Toeplitz structure of the model matrices and of the distortion matrices in the matrix-vector formulations of the original image and of the noisy blurred observation are approximated by circulant matrices, these matrices can be diagonalized by means of the FFT. Consequently, a parallel set of N dynamical models suitable for the derivation of N low-order vector Kalman filters in the transform domain is obtained. In this way, the number of computations is reduced from the order of O(N4) to that of O(N^{2} \log_{2} N) for N × N images.

An optimal bit allocation algorithm for sub-band coding

An optimal bit allocation algorithm is presented that is suitable for all practical situations. Each source to be coded is assumed to have its own set of admissible quantizers (which can be either scalar or vector quantizers) which do not need to have integer bit rates. The distortion versus rate characteristic of each quantizer set may have an arbitrary shape. The algorithm is very simple in structure and can be applied to any practical coding scheme (such as a subband coder) that needs dynamic bit allocation.<<ETX>>

Real-Time Labeling of MPEG-2 Compressed Video

Digital mass recording devices for video data will enter the consumer market soon. Service providers are reluctant to offer services in digital form because of their fears for unrestricted duplication and dissemination. Therefore adequate copy protection systems should be developed, most likely based on labeling techniques. In this paper we propose two different techniques for the real-time labeling of digital video. Both methods embed the label information directly into an MPEG compressed video bitstream. The first method embeds the label by changing variable length codes in the bitstream. The second method discards some of the high frequency DCT-coefficients of the bitstream to embed the label. The first technique is computationally less expensive than the second one, however, the second one is more robust against attacks to remove the label. We show that the label can still be extracted after MPEG re-encoding at a lower bit-rate.