The authors propose a novel adaptive spatiotemporal filter, called the adaptive weighted averaging (AWA) filter, for effective noise suppression in image sequences without introducing visually disturbing blurring artifacts. Filtering is performed by computing the weighted average of image values within a spatiotemporal support along the estimated motion trajectory at each pixel. The weights are determined by optimizing a well defined mathematical criterion, which provides an implicit mechanism for deemphasizing the contribution of the outlier pixels within the spatiotemporal filter support to avoid blurring. The AWA filter is therefore particularly well suited for filtering sequences that contain segments with abruptly changing scene content due to, for example, rapid zooming and changes in the view of the camera. The performance of the proposed AWA filter is compared with that of the spatiotemporal, local linear minimum mean square error (LMMSE) filtering. The results demonstrate that the proposed AWA filter-outperforms the LMMSE filter, especially in the cases of low signal-to-noise ratios and abruptly varying scene content. >

Adaptive motion-compensated filtering of noisy image sequences

A watermarking system comprises an encoding data processor operable to
generate at least one marked version of an original item of material by introducing one
of a predetermined set of code words into a copy of the original material item. The
encoding data processor is arranged to form other code words of the set by cyclically
shifting a first code word. The system includes a detecting data processor operable to
identify the code word in the marked material item. The detecting data processor is
operable to recover the code word from the marked material item and to form a Fourier
transform of the recovered code word and a Fourier transform of the first code word of
the set. The data detecting processor forms correlation samples by forming an inverse
transform of a combination of the recovered and the first code word. Each of the
correlation value samples provides the correlation value for one of the set of code
words. A computationally efficient way of calculating the correlation for each code
word in the set is thereby provided. As a result a time taken to detect a code word
present in a marked material item is improved. The watermarking system finds
particular application in identifying a point of distribution of pirate copies of video
material generated by capturing the watermarked image, using, for example, a
camcorder in a cinema.

Data processing apparatus and method

The history of segmentation of digital images using computers could be traced back 40 years. Since then, this field has evolved very quickly and has undergone great change. In this chapter, the position of image segmentation in the general scope of image techniques is first introduced; the formal definition and extension of image segmentation as well as three layers of research on image segmentation are then explained. Based on the introduction and explanations, statistics for a number of developed algorithms is provided, the scheme for classifying different segmentation algorithms is discussed and a summary of existing survey papers for image segmentation is presented. These discussions provide a general rendering of research and development of image segmentation in the last 40 years.

An Overview of Image and Video Segmentation in the Last 40 Years

An algorithm is presented for the reconstruction of PET images using prior anatomical information derived from MR images of the same subject. The cross-entropy or Kullback-Leiber distance is a measure of dissimilarity between two images. We propose to reconstruct PET images by minimizing a weighted sum of two cross-entropy terms. The first is the cross-entropy between the measured emission data and the forward projection of the current estimate of the PET image. Minimizing this term alone is equivalent to the ML-EM reconstruction. The second term is the cross-entropy between the current estimate of the PET image and a prior image model which incorporates anatomical information derived from registered MR images. A weighting parameter determines the relative emphasis given to the emission data and the prior model in the reconstruction. Details of this algorithm are presented as well as test reconstructions for real and simulated data. The performance of the algorithm was evaluated with respect to errors in prior anatomical information. The algorithm provided significant improvement in the quality of reconstructed images as compared with the ML-EM reconstruction technique. The reconstructed images had higher resolution as compared with the images obtained from MAP-like reconstructions which do not utilize anatomical information. The algorithm displayed robustness with respect to errors in prior anatomical information.

https://iopscience.iop.org/article/10.1088/0031-9155/41/11/018/pdf

Minimum cross-entropy reconstruction of PET images using prior anatomical information

A nonlinear operator is presented that is able to effectively attenuate the noise that corrupts an image while introducing small distortions on the image details. It is described by a rational function, i.e., by the ratio of two polynomials in the input variables. Notwithstanding its simplicity, this operator proves to be more powerful than conventional methods for many noise distributions.

T. G. Campbell

Papers

A quantitative comparison of edge-preserving smoothing techniques