Polyphase adaptive filter banks for fingerprint image compression

A perfect reconstruction polyphase filter bank structure is presented in which the filters adapt to the changing input conditions. The use of such a filter bank leads to higher compression results for images containing sharp edges such as fingerprint images

Polyphase adaptive filter banks for subband decomposition

Subband decomposition is widely used in signal processing applications including image and speech compression. In most practical cases, the goal is to obtain subband signals that are suitable for data compression. In this paper, we present Perfect Reconstruction (PR) polyphase filter bank structures in which the filters adapt to the changing input conditions. This leads to higher compression results for images containing sharp edges, text, and subtitles

The General Flow-Adaptive Filter : With Applications to Ultrasound Image Sequences

While image filtering is limited to two dimensions, the filtering of image sequences can utilize three dimensions; two spatial and one temporal. Unfortunately, simple extensions of common two-dimensional filters into three dimensions yield undesirable motion blurring of the images. This thesis addresses this problem and introduces a novel filtering approach termed the general flow-adaptive filter. Most often a three-dimensional filter can be visualized as a cubic lattice shifted over the data, and at each point the element corresponding to the central coordinate is replaced with a new value based entirely on the values inside the lattice. The general principle of the flow-adaptive approach is to spatially adapt the entire filter lattice to possibly complex spatial movements in the temporal domain by incorporating local flow-field estimates. Results using the flow-adaptive technique on five filters the temporal discontinuity filter, a tensor-based adaptive filter, the average, the median and a Gaussianshaped convolution filter are presented. Both ultrasound image sequences and synthetic data sets were filtered. An edge-adaptive normalized mean-squared error is used as performance metric on the filtered synthetic sets, and the error is shown to be substantially reduced using the flow-adaptive technique, as much as halved in many instances. There are even indications that simple Gaussian-shaped convolution filters can outperform larger and more complex adaptive filters by implementing the flow-adaptive procedure. For the ultrasound image sequences, the filters adopting the flow-adaptive principles had outputs with less motion blur and sharper contrast compared to the outputs of the non-flow-adaptive filters. At the cost of flow estimation, the flow-adaptive approach substantially improves the performance of all the filters included in this study