Research On Data Preprocessing Methods For Volume Visualization

Volume visualization which is widely used in medicine,geological,scientific simulation and other fields,can directly display the internal characteristics of feature information,and help users to further analyze and process the datasets.As one of the important research contents of scientific visualization,the data filtering method has been widely concerned.How to construct high quality data and improve the transmission efficiency and rendering quality of visualization is always an important research topic in scientific visualization.However,noise is often introduced in the data acquisition process,and the noise usually pollutes important features,which makes intensity-based analysis methods based fail.The characteristics are often not obvious through the transfer function.And the existing methods are usually in the mapping and rendering stage for feature enhancement processing.With the size of the datasets growing,especially the need for remote transmission of data,rendering efficiency is low.In the view of the above-mentioned problems in the data filtering stage,this paper puts forward to the corresponding improvement methods,which further enhances the visualization and analysis efficiency.This paper aims to study the efficient methods of data filtering,and propose novel solutions and ideas for specific data denoising,feature enhancement and rendering,so as to improve the efficiency of data visualization.The main research results include:In volume visualization,noise in regions of homogeneous material and at boundaries between different materials poses a great challenge in extracting,analyzing and rendering features of interest.In this paper,we present a novel volume denoising and smoothing method based on the L0 gradient minimization framework.This framework globally controls how many voxels with a non-zero gradient are in the result in order to approximate important features' structures in a sparse way.This procedure can be solved quickly by the alternating optimization strategy with half-quadratic splitting.While the proposed ovolume gradient minimization method can effectively remove noise in homogeneous materials,a blurring-sharpening strategy is proposed to diminish noise or smooth local details on the boundaries.This generates salient features with smooth boundaries and visually pleasing structures.We compare our method with the bilateral filter and anisotropic diffusion,and demonstrate the effectiveness and.efficiency of our method with several volumes in different modalities.Feature enhancement is important for the interpretation of complex structures and the detection of local details in volume visualization.In this paper,we present a simple and effective method based on Saliency Isosurface Enhancement Operator to enhance local contrast of features.Our method first converts volume to mesh data,and the important data in mesh will be enhanced,finally,the mesh will back into volume data by interpolation method.Such deformations exclude local rotations,avoiding harmful visual distortions,and they are efficient.Furthermore,it is particularly attractive for focus+context visualization of multiple features.We demonstrate the effectiveness and efficiency of our method with several volume datasets from medical applications and scientific simulations.Many fields,such as medicine and biology,are producing an increasingly large volume using high-resolution digital imaging techniques,and this makes effective data analysis and visualization of these volumes more and more difficult.Volume reduction,decreasing the volume size,is one of the promising directions to solve this challenge for interactive volume visualization.In this paper,we present an automatic volume data reduction method called surface carving.It intelligently removes contextual voxels while preserving important features,and finally generates an optimal volume at the desired reduction size or rate.For large volume data sets,a multilevel banded method is introduced to gracefully overcome the memory limit and speed up volume reduction.We compare our technique with traditional cropping and scaling approaches and demonstrate the effectiveness and efficiency of our method with several volume data sets.