Bilateral filter in image processing

The bilateral filter is a nonlinear filter that does spatial averaging without smoothing edges. It has shown to be an effective image denoising technique. It also can be applied to the blocking artifacts reduction. An important issue with the application of the bilateral filter is the selection of the filter parameters, which affect the results significantly. Another research interest of bilateral filter is acceleration of the computation speed. There are three main contributions of this thesis. The first contribution is an empirical study of the optimal bilateral filter parameter selection in image denoising. I propose an extension of the bilateral filter: multi resolution bilateral filter, where bilateral filtering is applied to the low-frequency sub-bands of a signal decomposed using a wavelet filter bank. The multi resolution bilateral filter is combined with wavelet thresholding to form a new image denoising framework, which turns out to be very effective in eliminating noise in real noisy images. The second contribution is that I present a spatially adaptive method to reduce compression artifacts. To avoid over-smoothing texture regions and to effectively eliminate blocking and ringing artifacts, in this paper, texture regions and block boundary discontinuities are first detected; these are then used to control/adapt the spatial and intensity parameters of the bilateral filter. The test results prove that the adaptive method can improve the quality of restored images significantly better than the standard bilateral filter. The third contribution is the improvement of the fast bilateral filter, in which I use a combination of multi windows to approximate the Gaussian filter more precisely

Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200 - 500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging

A learning-by-example method for reducing BDCT compression artifacts in high-contrast images.

Wang, Guangyu.Thesis submitted in: December 2003.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 70-75).Abstracts in English and Chinese.Chapter 1 --- Introduction --- p.1Chapter 1.1 --- BDCT Compression Artifacts --- p.1Chapter 1.2 --- Previous Artifact Removal Methods --- p.3Chapter 1.3 --- Our Method --- p.4Chapter 1.4 --- Structure of the Thesis --- p.4Chapter 2 --- Related Work --- p.6Chapter 2.1 --- Image Compression --- p.6Chapter 2.2 --- A Typical BDCT Compression: Baseline JPEG --- p.7Chapter 2.3 --- Existing Artifact Removal Methods --- p.10Chapter 2.3.1 --- Post-Filtering --- p.10Chapter 2.3.2 --- Projection onto Convex Sets --- p.12Chapter 2.3.3 --- Learning by Examples --- p.13Chapter 2.4 --- Other Related Work --- p.14Chapter 3 --- Contamination as Markov Random Field --- p.17Chapter 3.1 --- Markov Random Field --- p.17Chapter 3.2 --- Contamination as MRF --- p.18Chapter 4 --- Training Set Preparation --- p.22Chapter 4.1 --- Training Images Selection --- p.22Chapter 4.2 --- Bit Rate --- p.23Chapter 5 --- Artifact Vectors --- p.26Chapter 5.1 --- Formation of Artifact Vectors --- p.26Chapter 5.2 --- Luminance Remapping --- p.29Chapter 5.3 --- Dominant Implication --- p.29Chapter 6 --- Tree-Structured Vector Quantization --- p.32Chapter 6.1 --- Background --- p.32Chapter 6.1.1 --- Vector Quantization --- p.32Chapter 6.1.2 --- Tree-Structured Vector Quantization --- p.33Chapter 6.1.3 --- K-Means Clustering --- p.34Chapter 6.2 --- TSVQ in Artifact Removal --- p.35Chapter 7 --- Synthesis --- p.39Chapter 7.1 --- Color Processing --- p.39Chapter 7.2 --- Artifact Removal --- p.40Chapter 7.3 --- Selective Rejection of Synthesized Values --- p.42Chapter 8 --- Experimental Results --- p.48Chapter 8.1 --- Image Quality Assessments --- p.48Chapter 8.1.1 --- Peak Signal-Noise Ratio --- p.48Chapter 8.1.2 --- Mean Structural SIMilarity --- p.49Chapter 8.2 --- Performance --- p.50Chapter 8.3 --- How Size of Training Set Affects the Performance --- p.52Chapter 8.4 --- How Bit Rates Affect the Performance --- p.54Chapter 8.5 --- Comparisons --- p.56Chapter 9 --- Conclusion --- p.61Chapter A --- Color Transformation --- p.63Chapter B --- Image Quality --- p.64Chapter B.1 --- Image Quality vs. Quantization Table --- p.64Chapter B.2 --- Image Quality vs. Bit Rate --- p.66Chapter C --- Arti User's Manual --- p.68Bibliography --- p.7

Model Augmented Deep Neural Networks for Medical Image Reconstruction Problems

Solving an ill-posed inverse problem is difficult because it doesn\u27t have a unique solution. In practice, for some important inverse problems, the conventional methods, e.g. ordinary least squares and iterative methods, cannot provide a good estimate. For example, for single image super-resolution and CT reconstruction, the results of these conventional methods cannot satisfy the requirements of these applications. While having more computational resources and high-quality data, researchers try to use machine-learning-based methods, especially deep learning to solve these ill-posed problems. In this dissertation, a model augmented recursive neural network is proposed as a general inverse problem method to solve these difficult problems. In the dissertation, experiments show the satisfactory performance of the proposed method for single image super-resolution, CT reconstruction, and metal artifact reduction

Removal Of Blocking Artifacts From JPEG-Compressed Images Using Neural Network

The goal of this research was to develop a neural network that will produce considerable improvement in the quality of JPEG compressed images, irrespective of compression level present in the images. In order to develop a computationally efficient algorithm for reducing blocky and Gibbs oscillation artifacts from JPEG compressed images, we integrated artificial intelligence to remove blocky and Gibbs oscillation artifacts. In this approach, alpha blend filter [7] was used to post process JPEG compressed images to reduce noise and artifacts without losing image details. Here alpha blending was controlled by a limit factor that considers the amount of compression present, and any local information derived from Prewitt filter application in the input JPEG image. The outcome of modified alpha blend was improved by a trained neural network and compared with various other published works [7][9][11][14][20][23][30][32][33][35][37] where authors used post compression filtering methods

Wavelet-Based Kernel Construction for Heart Disease Classification

© 2019 ADVANCES IN ELECTRICAL AND ELECTRONIC ENGINEERINGHeart disease classification plays an important role in clinical diagnoses. The performance improvement of an Electrocardiogram classifier is therefore of great relevance, but it is a challenging task too. This paper proposes a novel classification algorithm using the kernel method. A kernel is constructed based on wavelet coefficients of heartbeat signals for a classifier with high performance. In particular, a wavelet packet decomposition algorithm is applied to heartbeat signals to obtain the Approximation and Detail coefficients, which are used to calculate the parameters of the kernel. A principal component analysis algorithm with the wavelet-based kernel is employed to choose the main features of the heartbeat signals for the input of the classifier. In addition, a neural network with three hidden layers in the classifier is utilized for classifying five types of heart disease. The electrocardiogram signals in nine patients obtained from the MIT-BIH database are used to test the proposed classifier. In order to evaluate the performance of the classifier, a multi-class confusion matrix is applied to produce the performance indexes, including the Accuracy, Recall, Precision, and F1 score. The experimental results show that the proposed method gives good results for the classification of the five mentioned types of heart disease.Peer reviewedFinal Published versio