High-boost Filter Research Articles

Geometric surface processing via normal maps

We propose that the generalization of signal and image processing to surfaces entails filtering the normals of the surface, rather than filtering the positions of points on a mesh. Using a variational strategy, penalty functions on the surface geometry can be formulated as penalty functions on the surface normals, which are computed using geometry-based shape metrics and minimized using fourth-order gradient descent partial differential equations (PDEs). In this paper, we introduce a two-step approach to implementing geometric processing tools for surfaces: (i) operating on the normal map of a surface, and (ii) manipulating the surface to fit the processed normals. Iterating this two-step process, we efficiently can implement geometric fourth-order flows by solving a set of coupled second-order PDEs. The computational approach uses level set surface models; therefore, the processing does not depend on any underlying parameterization. This paper will demonstrate that the proposed strategy provides for a wide range of surface processing operations, including edge-preserving smoothing and high-boost filtering. Furthermore, the generality of the implementation makes it appropriate for very complex surface models, for example, those constructed directly from measured data.

IMECO: A Reconfigurable FPGA-based Image Enhancement Co-Processor Framework

In this paper, we present a way to improve the computational speed of image contrast enhancement using low-cost FPGA-based hardware primarily targeted to X-ray images. In particular, we consider an enhancement method that consists of filtering followed by histogram modification. Filtering is done via the high boost filter (HBF) which is based on unsharp masking, and the histogram modification which is based on global histogram equalization (GHE). An image enhancement co-processor, IMECO, concept is proposed that enables efficient hardware implementation of enhancement procedures and hardware/software co-design to achieve high-performance low-cost solutions. The co-processor runs on an FPGA prototyping ISA-bus board. At this stage it consists of two hardware functional units that implement HBF and GHE and can be downloaded onto the board sequentially or reside on the board at the same time. These units represent an embryo of virtual hardware units that form a library of image enhancement algorithms. These algorithms can be easily integrated into software templates. In our trials with chest X-ray images, performance improvement over software-only implementations is more than two orders of magnitude, thus providing real-time or near-real-time image enhancement as required in target applications.

A method for detecting microcalcifications in digital mammograms.

Microcalcification clusters are often an important indicator for the detection of malignancy in mammograms. In many cases, microcalcifications are the only indication of a malignancy. However, the detection of microcalcifications can be a difficult process. They are small and can be embedded in dense tissue. This paper presents a method for automatically detecting microcalcifications. We utilize a high-boost filter to suppress background clutter enabling segmentation even in very dense breast tissue. We then use a threshholding and region growing technique to extract candidate microcalcifications. Likely microcalcifications are then identified by a linear classifier. We apply this method to images selected from the LLNL/UCSF Digital Mammogram Library, and produce a receiver operating characteristic (ROC) curves to detail the trade-off between probability of detection and false alarms. Finally, we exam the ability to properly select a threshold to achieve a desired probability of detection based upon a training set. This is a US government work. There are no restrictions on its use.