- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - Shanghai, China (2018.9.6-2018.9.7)] 2018 International Symposium in Sensing and Instrumentation in IoT Era (ISSI) - Image Denoising Using Asymmetric Gaussian Mixture Models
摘要： Finite mixture models are widely applied in image denoising because they have sound mathematical basis and the results are interpretable. In a manner of speaking, they can give a mathematical description of natural images by clustering. Usually assume that per-component of natural images follow a mixture of Gaussian(GMM) when doing image denoising. However, it is well- know that most of natural images are intricate and of which the distribution is highly non-Gaussian. So there remain problems that GMM cannot fix. In this paper, we introduce the asymmetric Gaussian mixture models into the finite mixture model, in which GMM is a special case. Asymmetric Gaussian Mixture (AGM) can model asymmetric distribution which is more conform to the data of natural images. We do some experiments in image denoising under different noise scales and types. The AGM can have better results compare to The GMM.
关键词： image denoising,priors,asymmetric Gaussian mixture models,expected patch log likelihood
更新于2025-09-10 09:29:36
-
[IEEE 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) - Pune (2017.8.17-2017.8.18)] 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA) - An Improved Image Denoising Using Spatial Adaptive Mask Filter for Medical Images
摘要： Medical images are affected by the noise and image quality is degraded especially in magnetic Resonance Imaging (MRI) and ultrasound imaging. These images are mostly containing salt and pepper noise. It is critical to remove the noise from the medical images because the important information may affect. In this paper, a robust image denoising technique called spatial adaptive mask filtering technique has been proposed. The proposed algorithm is able to remove the noise from the MRI and ultrasound images while retaining the important information of the image. Experimentation results show that the quality of the images are improved and it is measured in terms of Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE). The comparative analysis of mean, median and adaptive median filter has been presented.
关键词： medical image denoising,Adaptive median,mean,median,spatial adaptive mask,filter component
更新于2025-09-10 09:29:36
-
[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC, Canada (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Fast, Robust, and Accurate Image Denoising via Very Deeply Cascaded Residual Networks
摘要： Patch based image modelings have shown great potential in image denoising. They mainly exploit the nonlocal self-similarity (NSS) of either input degraded images or clean natural ones when training models, while failing to learn the mappings between them. More seriously, these algorithms have very high time complexity and poor robustness when handling images with different noise variances and resolutions. To address these problems, in this paper, we propose very deeply cascaded residual networks (VDCRN) to build the precise relationships between the noisy images and their corresponding noise-free ones. It adopts a new residual unit with an identity skip connection (shortcut) to make training easy and improve generalization. The introduction of shortcut is helpful to avoid the problem of gradient vanishing and preserve more image details. By cascading three such residual units, we build the VDCRN to deploy deeper and larger convolutional networks. Based on such a residual network, our VDCRN achieves very fast speed and good robustness. Experimental results demonstrate that our model outperforms a lot of state-of-the-art denoising algorithms quantitively and qualitively.
关键词： image denoising,nonlocal self-similarity,cascaded residual networks
更新于2025-09-09 09:28:46
-
[IEEE 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) - Ankara, Turkey (2018.10.19-2018.10.21)] 2018 2nd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT) - An Efficient Retinal Blood Vessel Segmentation using Morphological Operations
摘要： The structure of retinal vessel carries information about many diseases. It is difficult to analyze this complex structure by human eye. Additionally, it has time-consuming process. In this study, an extremely lower complex and more successful retinal blood vessel segmentation method is proposed via using morphological operators. Colorful retinal images are divided into red, green and blue channels. Green channel is preferred for segmentation on the account of including clear details about retinal vessels. Then, adaptive threshold with 5x5 Gaussian window is applied in order to obtain clean vessel geometry. In the next step, retinal image is sharpened and then, 3x3 wiener filter is applied to it. After wiener filter, some noise in the image decreases but retinal image pixels soften. Therefore, Otsu thresholding is applied to softened images. Finally, morphological operation is performed on gray level images. The proposed method is implemented on test images in DRIVE database. The process time of our method is 0.7-0.8 second and it is faster than other methods. 95,61% accuracy, 85.096% sensitivity and 96.33% specificity rates are obtained.
关键词： image texture analysis,Biomedical image processing,image denoising,segmentation,image edge detection
更新于2025-09-04 15:30:14
-
[IEEE 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Nara, Japan (2018.10.9-2018.10.12)] 2018 IEEE 7th Global Conference on Consumer Electronics (GCCE) - Shape-aware Medical Image Enhancement by Weighted Total Variation
摘要： In this paper, we propose a sharpening method for medical images combining weights on each pixel and Total Variation regularization. When weighting properly on each pixel in Total Variation regularization, information loss in optimization can be prevented. In the proposed method, weighting for each pixel is calculated from emphasis processing on an image and edge information, and Total Variation regularization is performed using weight information. As a result, noise removal and sharpening can be performed at the same time. Moreover, by comparing the proposed method with the conventional method, qualitative evaluation is carried out, and the effectiveness is shown.
关键词： Image enhancement,Image denoising,Medical diagnostic imaging,Biomedical imaging
更新于2025-09-04 15:30:14
-
A new development of non-local image denoising using fixed-point iteration for non-convex ?p sparse optimization
摘要： We proposed a new efficient image denoising scheme, which mainly leads to four important contributions whose approaches are different from existing ones. The first is to show the equivalence between the group-based sparse representation and the Schatten-p norm minimization problem, so that the sparsity of the coefficients for each group can be measured by estimating the underlying singular values. The second is that we construct the proximal operator for sparse optimization in ?p space with p 2 (0, 1] by using fixed-point iteration and obtained a new solution of Schatten-p norm minimization problem, which is more rigorous and accurate than current available results. The third is that we analyze the suitable setting of power p for each noise level σ = 20, 30, 50, 60, 75, 100, respectively. We find that the optimal value of p is inversely proportional to the noise level except for high level of noise, where the best values of p are 1 and 0.95, when the noise levels are respectively 75 and 100. Last we measure the structural similarity between two image patches and extends previous deterministic annealing-based solution to sparsity optimization problem through incorporating the idea of dictionary learning. Experimental results demonstrate that for every given noise level, the proposed Spatially Adaptive Fixed Point Iteration (SAFPI) algorithm attains the best denoising performance on the value of Peak Signal-to-Noise Ratio (PSNR) and structure similarity (SSIM), being able to retain the image structure information, which outperforms many state-of-the-art denoising methods such as Block-matching and 3D filtering (BM3D), Weighted Nuclear Norm Minimization (WNNM) and Weighted Schatten p-Norm Minimization (WSNM).
关键词： sparse optimization,Schatten-p norm,image denoising,proximal operator,fixed-point iteration
更新于2025-09-04 15:30:14
-
A Novel Algorithm for Image Denoising Using DT-CWT
摘要： This paper addresses image enhancement system consisting of image denoising technique based on Dual Tree Complex Wavelet Transform (DT-CWT) . The proposed algorithm at the outset models the noisy remote sensing image (NRSI) statistically by aptly amalgamating the structural features and textures from it. This statistical model is decomposed using DTCWT with Tap-10 or length-10 filter banks based on Farras wavelet implementation and sub band coefficients are suitably modeled to denoise with a method which is efficiently organized by combining the clustering techniques with soft thresholding - soft-clustering technique. The clustering techniques classify the noisy and image pixels based on the neighborhood connected component analysis(CCA), connected pixel analysis and inter-pixel intensity variance (IPIV) and calculate an appropriate threshold value for noise removal. This threshold value is used with soft thresholding technique to denoise the image .Experimental results shows that that the proposed technique outperforms the conventional and state-of-the-art techniques .It is also evaluated that the denoised images using DTCWT (Dual Tree Complex Wavelet Transform) is better balance between smoothness and accuracy than the DWT.. We used the PSNR (Peak Signal to Noise Ratio) along with RMSE to assess the quality of denoised images.
关键词： Soft-Clustering,Image Denoising,PSNR,Tap-10 Filter banks,DTCWT
更新于2025-09-04 15:30:14
-
High-Dimensional Mixture Models for Unsupervised Image Denoising (HDMI)
摘要： This work addresses the problem of patch-based image denoising through the unsupervised learning of a probabilistic high-dimensional mixture model on the noisy patches. The model, called HDMI, proposes a full modeling of the process that is supposed to have generated the noisy patches. To overcome the potential estimation problems due to the high dimension of the patches, the HDMI model adopts a parsimonious modeling which assumes that the data live in group-specific subspaces of low dimensionalities. This parsimonious modeling allows us in turn to get a numerically stable computation of the conditional expectation of the image which is applied for denoising. The use of such a model also permits us to rely on model selection tools, such as BIC, to automatically determine the intrinsic dimensions of the subspaces and the variance of the noise. This yields a denoising algorithm that can be used both when the noise level is known and is unknown.
关键词： image denoising,parsimonious mixture model,model selection,high-dimensional clustering,intrinsic dimension estimation,patch-based representation
更新于2025-09-04 15:30:14
-
An effective image denoising using PPCA and classification of CT images using artificial neural networks
摘要： The main aim of denoising is to remove the noise while recollecting as much possible important signal features. This appears to be very simple when considered under practical situations, where the type of images and noises are all variable parameters. This paper deals with removal of combination of noises from image and classification of normal and abnormal images. At first phase, median filter is used to remove the noises present in the images. To improve the denoised output, we are using PSM and PPCA with morphological operations, filter and region props. In the second phase, to analyse the denoised output, neural network-based classification is proposed. The use of artificial intelligent techniques for classification shows a great potential in this field. Hence the performance of neural network classifier is estimated in terms of training performance and classification accuracy and is compared with the existing method to show the system is effective.
关键词： GLCM,median filter,Gaussian noise,pixel surge model,CT images,neural networks,image denoising,PPCA
更新于2025-09-04 15:30:14