Edge-preserving Smoothing Algorithm Research Articles

Comparisons of Split-linear Fitting of Wind Curves

The detection of slope change points in wind curves depends on linear curve-fitting. Hall and Titterington’s algorithm based on smoothing is adapted and compared to a Bayesian method of curve-fitting. After prior spline smoothing of the data, the algorithms are tested and the errors between the split-linear fitted wind and the real one are estimated. In our case, the adaptation of the edge-preserving smoothing algorithm gives the same good performance as automatic Bayesian curve-fitting based on a Monte Carlo Markov chain algorithm yet saves computation time.

Footprint removal from seismic data with residual dictionary learning

Dictionary learning (DL) is a machine learning technique that can be used to find a sparse representation of a given data set by means of a relatively small set of atoms, which are learned from the input data. DL allows for the removal of random noise from seismic data very effectively. However, when seismic data are contaminated with footprint noise, the atoms of the learned dictionary are often a mixture of data and coherent noise patterns. In this scenario, DL requires carrying out a morphological attribute classification of the atoms to separate the noisy atoms from the dictionary. Instead, we have developed a novel DL strategy for the removal of footprint patterns in 3D seismic data that is based on an augmented dictionary built upon appropriately filtering the learned atoms. The resulting augmented dictionary, which contains the filtered atoms and their residuals, has a high discriminative power in separating signal and footprint atoms, thus precluding the use of any statistical classification strategy to segregate the atoms of the learned dictionary. We filter the atoms using a domain transform filtering approach, a very efficient edge-preserving smoothing algorithm. As in the so-called coherence-constrained DL method, the proposed DL strategy does not require the user to know or adjust the noise level or the sparsity of the solution for each data set. Furthermore, it only requires one pass of DL and is shown to produce successful transfer learning. This increases the speed of the denoising processing because the augmented dictionary does not need to be calculated for each time slice of the input data volume. Results on synthetic and 3D public-domain poststack field data demonstrate effective footprint removal with accurate edge preservation.

Rolling Guidance Filtering-Orientated Saliency Region Extraction Method for Visible and Infrared Images Fusion

Different image types can be obtained by different sensors, but all the useful information cannot be extracted from a single image. Infrared images can capture the heat source information of scene targets in low light or severe weather conditions. Visible images provide more detail information about the scene. To obtain the rich image information, we propose a visible and infrared images fusion method based on rolling guidance filtering and saliency region extraction in this paper. A multi-scale image decomposition framework is built by using the edge preserving-smoothing algorithm. The image is decomposed into one base layer with different scales and several detail layers. Meanwhile, the saliency region extraction is implemented on each decomposition layer by combining with the rolling guidance filtering. Weight reconstruction is adopted to obtain the final fusion result. The results show that the proposed algorithm has good subjective and objective evaluation results, better fusion performance and robustness compared to other state-of-the-art fusion methods.

Content Adaptive Image Detail Enhancement

Detail enhancement is required by many problems in the fields of image processing and computational photography. Existing detail enhancement algorithms first decompose a source image into a base layer and a detail layer via an edge-preserving smoothing algorithm, and then amplify the detail layer to produce a detail-enhanced image. In this letter, we propose a new <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm based detail enhancement algorithm which generates the detail-enhanced image directly. The proposed algorithm preserves sharp edges better than an existing <i xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">L</i> <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> norm based algorithm. Experimental results show that the proposed algorithm reduces color distortion in the detail-enhanced image, especially around sharp edges.

Retaining Local Image Information in Gamut Mapping Algorithms

Our topic is the potential of combining global gamut mapping with spatial methods to retain the percepted local image information in gamut mapping algorithms. The main goal is to recover the original local contrast between neighboring pixels in addition to the usual optimization of preserving lightness, saturation, and global contrast. Special emphasis is placed on avoiding artifacts introduced by the gamut mapping algorithm itself. We present an unsharp masking technique based on an edge-preserving smoothing algorithm allowing to avoid halo artifacts. The good performance of the presented approach is verified by a psycho-visual experiment using newspaper printing as a representative of a small destination gamut application. Furthermore, the improved mapping properties are documented with local mapping histograms.

Comparisons of Split-linear Fitting of Wind Curves

The detection of slope change points in wind curves depends on linear curve-fitting. Hall and Titterington's algorithm based on smooth- ing is adapted and compared to a Bayesian method of curve-fitting. After prior spline smoothing of the data, the algorithms are tested and the er- rors between the split-linear fitted wind and the real one are estimated. In our case, the adaptation of the edge-preserving smoothing algorithm gives the same good performance as automatic Bayesian curve-fitting based on a Monte Carlo Markov chain algorithm yet saves computation time. This study is aimed at the improvement of the aircraft autopilot concep- tion process. The autopilot allows landings in bad weather conditions and must guarantee passengers safety, touchdown comfort, and precision. We study in par- ticular the influence of wind during automatic landing. We focus on the effect of the linear wind components in the last 30 seconds to show that they are a decisive factor in touchdown precision. This is achieved by comparing simulated landings with either a real wind or its piecewise linear approximation. This has led us to develop a method of split-linear fitting based on slope change detection adapted to our data. This method is similar to those proposed by Jones (1998) which aim at predicting the influence of discrete gusts on linear systems. Slope change detection is associated with edge and peak detection. Two kinds of method can be adapted : a classic one, based on smoothing and a Bayesian one based on Monte Carlo Markov Chains. Hall and Titterington (1992) pro- posed an edge-preserving smoothing algorithm. It is based on edge detection by comparisons of three smoothings. The aim of this method is to compute, for each given point, three smoothed estimates of the function, based on the data to the right, to the left and on both sides of the point. Each discontinuity is asso- ciated with a local maximum of the difference between the three fits. Wu and Chu (1993) took this algorithm and modified it using kernel smoothing instead

A novel approach to the classification of regional-scale Radar mosaics for tropical vegetation mapping

The Global Rain Forest Mapping (GRFM) radar mosaics, generated from L-band Japanese Earth Resources Satellite 1 imagery downsampled to 100-m pixel size, provide a two-season spatially continuous coverage of the humid tropical ecosystems of the world. This paper presents a novel classification approach suitable for regional-scale vegetation mapping using the GRFM datasets. The mapping system consists of: 1) an application-dependent wavelet-based edge-preserving smoothing algorithm and 2) a two-stage per-pixel hybrid learning nearest multiple-prototype (NMP) classifier, whose unsupervised first stage is a per-pixel near-optimal vector quantizer, called enhanced Linde-Buzo-Gray (ELBG), recently proposed in pattern recognition literature. Identified as ENMP (NMP with ELBG), this novel classification approach is compared against two alternative systems in the classification of forest cover disturbances located across an area in the Amazon Basin. Surface classes of interest are primary forest, degraded forest, nonforest, and water bodies. Reference maps, derived from 30-m resolution Landsat Thematic Mapper imagery, are provided by the National Aeronautics and Space Administration and the Food and Agriculture Organization of the United Nations. Abundant quantitative and qualitative evidence shows that: 1) in a forest/nonforest data-mapping task, ENMP provides a testing accuracy of 87%, in line with training accuracies, i.e., the proposed method seems capable of generalizing well over the GRFM South America dataset and 2) among three competing approaches, ENMP provides the best compromise between ease of use, mapping accuracy, and computational time. Starting from these results, ENMP is employed to generate a swamp forest map of the whole Amazon Basin from the two-season GRFM radar mosaic of South America, in the context of the Global Land Cover project (GLC 2000).

Edge-preserving artifact-free smoothing with image pyramids

We present a hierarchical implementation of an edge-preserving smoothing algorithm on the 2 × 2 pyramid structure. The smoothed pixel values are chosen from the first three levels of the pyramid and the original image. The reduced resolution representations are analyzed in a top-down fashion by comparing the local variances with the corresponding global noise variance estimates. The global estimates are also computed in the pyramid. Close to edges the pixel values are obtained by adaptive least squares. The artifacts of region-based smoothing are eliminated by pixelwise averaging over a set of outputs obtained with the input image shifted within the 8 × 8 block of the level three parent.