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An FIR image interpolation filter design method based on properties of human vision

An FIR image interpolation filter design method based on pro...

引用

The 1994 1st IEEE International Conference on image processing

作者： Chen, Hong Ford, G.E. Center for Image Processing and Integrated Computing University of California Davis DavisCA95616 United States

image interpolation is an important image operation. It is commonly used in image enlargement to obtain a close-up view of the detail of an image. From sampling theory, an ideal low-pass filter can be used for image interpolation. However, ripples appear around image edges which are annoying to a human viewer. The authors introduce a new FIR image interpolation filter known as a perceptually weighted least square (PWLS) filter which is designed using both sampling theory and properties of human vision. The goal of this design approach is to minimize the ripple response around edges of the interpolated images and to best satisfy frequency response constraints. The interpolation results using the proposed approach are substantially better than those resulting from replication or bilinear interpolation, and are at least as good as and possibly better than that of cubic convolution interpolation. © 1994 IEEE.

关键词： Low pass filters

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Perceptual wiener filtering for image restoration 28

Perceptual wiener filtering for image restoration

引用

28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994

作者： Chen, Hong Ford, Gary E. CIPIC Center for Image Processing and Integrated Computing University of California DavisCA95616 United States

ISBN: (纸本)0818664053

We propose a perceptual Wiener filter for image restoration, a linear space-variant filter which accounts for the human visual system response to image details and noise in the vicinity of an edge. This filter provides a reduction in the ringing artifact observed in the vicinity of edges, when compared to the response of the widely used linear space-invariant classical Wiener filter. A fast, approximate implementation of the filter is discussed. © 1995 IEEE.

关键词： image reconstruction

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Mean curvature evolution and surface area scaling in image filtering 28

Mean curvature evolution and surface area scaling in image f...

引用

28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994

作者： El-Fallah, Adel I. Ford, Gary E. CIPIC Center for Image Processing and Integrated Computing University of California Davis DavisCA95616 United States

ISBN: (纸本)0818664053

We propose a geometrical surface representation for an image, and introduce a nonlinear adaptive filter that diffuses the surface in time at a speed proportional to the mean curvature. Linear variations in intensity (edges) are inclined planes of vanishing mean curvature, and are thus invariant. Noise is characterized by high mean curvature and will be diffused. We show that this diffusion resolves the conflict of removing noise while preserving edges. A novel nonlinear scale space filtering relating surface area to the diffusion speed is introduced resulting in very efficient algorithms. Experiments demonstrating excellent performance and efficiency are presented. © 1995 IEEE.

关键词： Adaptive filtering

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Segmentation-based image compression with enhanced treatment of textured regions 28

Segmentation-based image compression with enhanced treatment...

引用

28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994

作者： Hussain, Iftekhar Reed, Todd R. Department of Electrical and Computer Engineering Center for Image Processing and Integrated Computing University of California DavisCA95616 United States

ISBN: (纸本)0818664053

This paper presents a method to preprocess an image so that when segmented it yields a partitioning in which textured regions are approximated with a substantially reduced number of uniform regions (which is desirable for the coding). The segmentation method used to form this representation combines a Gaussian texture model and Gibbs-Markov contour model in order to find regions with boundaries which correspond closely to the objects in the image. Given the image segmentation, an approximation to the original image is generated by filling each region with its mean value. If higher quality reconstruction is desired, the quantized approximation error is also encoded. In order to exploit the reduced sensitivity of the human visual system to the error around edges (visual masking), the error is quantized using three nonlinear quantizers corresponding to the smoothly varying, textured, and remaining areas of the image, respectively. © 1995 IEEE.

关键词： Errors

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Perceptual Wiener filtering for image restoration

Perceptual Wiener filtering for image restoration

引用

Asilomar Conference on Signals, Systems & Computers

作者： Hong Chen G.E. Ford Center for Image Processing and Integrated Computing University of California Davis CA USA

Proposes a perceptual Wiener filter for image restoration, a linear space-variant filter which accounts for the human visual system response to image details and noise in the vicinity of an edge. This filter provides a reduction in the ringing artifact observed in the vicinity of edges, when compared to the response of the widely used linear space-invariant classical Wiener filter. A fast, approximate implementation of the filter is discussed.< >

关键词： Wiener filter image restoration Humans Degradation Nonlinear filters Additive noise Gaussian noise Filtering image processing Postal services

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The evolution of mean curvature in image filtering

The evolution of mean curvature in image filtering

引用

IEEE International Conference on image processing

作者： A.I. El-Fallah G.E. Ford CIPIC Center for Image Processing and Integrated Computing University of California Davis CA USA

A new formulation for inhomogeneous image diffusion is presented in which the image is regarded as a surface in 3-space. The evolution of this surface under diffusion is analyzed by classical methods of differential geometry. A nonlinear filtering theory is introduced in which only the divergence of the direction of the surface gradient is averaged. This averaging preserves edges and lines, as their direction is non-divergent, while noise is averaged since it does not have non-divergent consistency. Our approach achieves this objective by evolving the surface at a speed proportional to mean curvature leading to the minimization of the surface area and the imposition of regularity everywhere. Furthermore, we introduce a new filter that renders corners, as well as edges, invariant to the diffusion process. Experiments demonstrating the adequacy of this new theory are presented.< >

关键词： Filtering Geometry Filters image edge detection Smoothing methods Solid modeling Rendering (computer graphics) image processing Postal services Face

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Mean curvature evolution and surface area scaling in image filtering

Mean curvature evolution and surface area scaling in image f...

引用

Asilomar Conference on Signals, Systems & Computers

作者： A.I. El-Fallah G.E. Ford CIPIC Center for Image Processing and Integrated Computing University of California Davis CA USA

Proposes a geometrical surface representation for an image, and introduces a nonlinear adaptive filter that diffuses the surface in time at a speed proportional to the mean curvature. Linear variations in intensity (edges) are inclined planes of vanishing mean curvature, and are thus invariant. Noise is characterized by high mean curvature and will be diffused. The authors show that this diffusion resolves the conflict of removing noise while preserving edges. A novel nonlinear scale space filtering relating surface area to the diffusion speed is introduced resulting in very efficient algorithms. Experiments demonstrating excellent performance and efficiency are presented.< >

关键词： Adaptive filters image processing Additive noise Gaussian noise Postal services Nonlinear filters Filtering algorithms Geometry Noise reduction Signal to noise ratio

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An FIR image interpolation filter design method based on properties of human vision

An FIR image interpolation filter design method based on pro...

引用

IEEE International Conference on image processing

作者： Hong Chen G.E. Ford Center for Image Processing and Integrated Computing University of California슠Davis Davis CA USA

关键词： Finite impulse response filter Interpolation Design methodology Low pass filters Filtering theory image sampling Humans Least squares methods Frequency response Convolution

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Segmentation-based image compression with enhanced treatment of textured regions

Segmentation-based image compression with enhanced treatment...

引用

Asilomar Conference on Signals, Systems & Computers

作者： I. Hussain T.R. Reed Department of Electrical & Computer Engineering and the Center for Image Processing and Integrated Computing University of California Davis CA USA

关键词： image segmentation image coding image reconstruction Approximation error image processing image generation Filling Humans Visual system Entropy

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Analysis of texture images using robust fractal description

Analysis of texture images using robust fractal description

引用

1995 IEEE Southwest Symposium on image Analysis and Interpretation, IAI 1994

作者： Avadhanam, N. Mitra, S. Center for Image Processing and Integrated Computing Dept. of Electr. and Comput. Eng. California Univ. DavisCA95616 United States Computer Vision and Image Analysis Lab Dept. of Electrical Engineering Texas Tech University LubbockTX79401 United States

ISBN: (纸本)0818662506

A maximum likelihood estimation (MLE) method is used to estimate the fractal dimension of a number of natural texture images with and without the presence of noise. An additional texture measure which can be linked to the lacunarity measure is used to characterize natural textures since fractal dimension alone cannot totally characterize texture images. Segmentation of natural textures is successfully achieved by a k-means clustering algorithm using fractal dimension and the additional measure as representative features. © 1994 IEEE.

关键词： image texture

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