基于双向耦合扩散的自适应图像插值

提出了一种自适应的双向耦合扩散算法:沿着等照度线(边缘)的梯度方向实施反向扩散以增强边缘,而相反地沿切线方向实施正向扩散以去除人工锯齿。为了消除这2个相反的扩散力彼此之间的冲突,将此算法分裂为一种耦合的格式;同时,为了保持图像特征,利用图像的方向导数局部地调整非线性扩散系数。最后将上述算法应用于图像插值。实验结果显示,相比于传统的图像插值算法和一些相关的偏微分方程模型,此图像插值算法可以显著地提高被插值图像的视觉质量。     

Fuzzy-adapted linear interpolation algorithm for image zooming

This work presents a novel fuzzy linear interpolation algorithm with application in image zooming. Fuzzy logics are employed to derive suitable weights for the neighboring samples in the interpolation formulae. By considering local gradients to calculate the weights, the accuracy of the interpolated value is improved. Additionally, a modification of the proposed algorithm based on the interpolation error theorem is developed to deal with images containing ridges and valleys. Both quantitative results obtained by measuring the peak signal-to-noise ratio (PSNR) and perceptual observations assessed the superior performance of the proposed algorithm and its modified version with respect to the state-of-the-art interpolation methods.     

An edge fusion scheme for image denoising based on anisotropic diffusion models

In this paper, we propose an enhanced anisotropic diffusion model. The improved model can classify finely image information as smooth regions, edges, corners and isolated noises by characteristic parameters and gradient variance parameter. And for different image information the eigenvalues of diffusion tensor are designed to conduct adaptive diffusion. Moreover, an edge fusion scheme is posed to preserve edges after denoising by combing different denoising and edge detection methods. Firstly, different denoising methods are applied for noisy image to obtain denoised images, and the best method among them is selected as main method. Then edge images of denoised images are obtained by edge detection methods. Finally, by fusing edge images together more integrated edges can be achieved to replace edges of denoised image obtained by main method. The experimental results show the proposed model can denoise meanwhile preserve edges and corners, and the edge fusion scheme is accurate and effective.     

基于结构成分双向扩散的图像插值算法

该文提出一种基于结构成分双向扩散的插值方法,有效地减小了插值图像的边缘扩散,从而获得更为清晰的边缘。该方法采用改进的耦合双向扩散滤波器对轮廓模板插值图像进行边缘增强。其中,为了使滤波器更精确地作用于边缘轮廓,利用形态成分分析(MCA)分离出初始插值图像中的结构分量再实行滤波;同时,改进双向扩散模型,使其能够根据边缘梯度自适应地调整边缘扩散程度,且更加柔和地控制梯度方向的像素值变化。实验结果表明,对比传统的插值方法、相关的边缘自适应插值方法以及几种应用普遍的商用软件,该方法获得的插值图像主、客观质量均有明显提升,不仅有效提高图像锐度,且边缘光滑、过渡自然,避免产生边缘锯齿和过度的人工效应。     

一种基于自适应双向扩散过程的无源毫米波图像去噪与增强

研究和设计了一种新型自适应双向扩散过程,对无源毫米波图像进行降噪和增强处理.根据图像的局部特征进行自适应扩散处理,在图像的同质区域进行各向同性扩散降低噪声,在图像的边缘处沿着图像边缘的切向方向进行正向扩散降低噪声,沿着图像边缘的法向方向进行反向扩散锐化边缘.通过对仿真图像和实测的91.5 GHz无源毫米波图像的实验表明...     

Spectral-spatial adaptive and well-balanced flow-based anisotropic diffusion for multispectral image denoising

Anisotropic diffusion can provide better compromise between noise reduction and edge preservation. In multispectral images, there exist different spatial local structures in the same band. Therefore, the levels of smoothing of anisotropic diffusion process should conform to both of image spectral and spatial features. In this paper, we present an effective denoising algorithm by integrating the spectral-spatial adaptive mechanism into a well-balanced flow (WBF) based anisotropic diffusion model, in which an adjustable weighted function is introduced to perform the appropriate levels of smoothing and enhancing according to different feature scales. Moreover, we make the fidelity term in the model to be adaptive by replacing the original noisy signal with the last evolution of the smoothed image. Consequently, the proposed algorithm can better control the diffusion behavior than traditional multispectral diffusion-based algorithms. The experimental results verify that our algorithm can improve visual quality of the image and obtain better quality indices.     

Region-based Anisotropic Diffusion with Soft Shock Filter for Adaptive Image Enhancement

A region-based anisotropic diffusion with soft shock filter is presented for image noise removal and edge sharpening. Two image processing steps are performed successively： smoothing and sharpening. An image is divided into different regions according to image features： edges, textures and details, and fiat areas. For edges, a shocktype backward diffusion is performed in the gradient direction to the isophote line （edge）, incorporating a forward diffusion in the isophote line direction; while for textures and details, a soft backward diffusion is done to enhance image features preserving a natural transition. Experiments on real images show that this method produces better visual results of the enhanced images than some relevant equations.     

Split Bregmanized anisotropic total variation model for image deblurring

In this paper, an effective image deblurring model is proposed to preserve sharp image edges by suppressing the stair-casing arising in the total variation (TV) based method by using the anisotropic total variation. To solve the difficult L1 norm problems, the split Bregman iteration is employed. Several synthetic degraded images are used for experiments. Comparison results are also made with total variation and nonlocal total variation based method. Experimental results show that the proposed method not only is robust to noise and different blur kernels, but also performs well on blurring images with more detailed textures, and the stair-casing effect is well suppressed.     

Image coarsening by using space-filling curve for decomposition-based image enhancement

We propose a novel space-filling curve based image coarsening method, which automatically extracts a base-layer from an input image while still preserving its structural context, meaningful details, et cetera. In the proposed method, specifically, a one-dimensional edge-preserving smoothing filter, which is called a vector ε-filter, is applied to an input image along a space-filling curve. In this regard, the space-filling curve is constructed by using a minimum spanning tree which extracts the structural context of the input image. This novel image coarsening approach is completely different from all conventional approaches employing any kind of two-dimensional filter window. Furthermore, this coarsening method can effectively produce an aggregation of texture details as well as enhance sharp edges, while preserving structural contexts such as thin lines and sharp corners. The main benefit of the coarsened image by the proposed method is its suitability for extracting fine features of an input image for decomposition-based image enhancement. In this paper, the structural-context-preserving image coarsening capability of the proposed method is verified by some results from experiments and examples. Then we show our new method’s characteristics in practical application to decomposition-based image enhancement by using some other examples.