Edge-preserving Image Research Articles

A hybrid regularization method combining Tikhonov with total variation for electrical resistance tomography

Electrical resistance tomography (ERT) is a promising measurement technique in industrial process imaging. However, image reconstruction in ERT is an ill-posed inverse problem. Regularization methods have been developed to solve the ill-posed inverse problem. Since the penalty term is a form of L2-norm, Tikhonov regularization method guarantees the stability of the solution, but it always makes the image edge oversmoothed. Total variation (TV) regularization method has good ability of preserving image edges. A hybrid regularization method, which combines Tikhonov with TV regularization method, is proposed to get better reconstructed images. The choice of the adaptive weighted parameter between TV and Tikhonov penalty term has been discussed in detail. In the proposed hybrid regularization method, the function of conductivity gradients is used as the adaptive weighted parameter to control automatically the weighting between the penalty terms from TV and Tikhonov regularization. For the model with sharp edges, the proportion of the penalty term from TV regularization is increased to preserve the edges, while for the model with smooth edges, the proportion of penalty term from Tikhonov regularization is increased to make the solution stable and robust to noise. Both simulation and experimental results of Tikhonov, TV and hybrid regularization method are shown respectively, which indicates that the hybrid regularization method can improve the reconstruction quality with sharp edges and is more robust to noise, and it is applicable for models with different edge characteristic.

Review on: Image De-noising using Bilateral Filter with Multilinear Discriminant Analysis and SVM

Noise removal from magnetic resonance images is important for further processing and visional analysis. Bilateral filter is known for its effective performance in edge-preserved image denoising. Here, an iterative bilateral filter is proposed for filtering the Rician noise in the magnitude magnetic resonance images. It improves the denoising efficiency. It also preserves the fine structures of the image. It also reduces the bias due to Rician noise. Thus we can preserve the quality of image. The quantitative analysis based on the standard metrics like peak signal-to-noise ratio and mean structural similarity index matrix. It shows the proposed method which performs better than the other recently proposed denoising methods for MRI.

PET reconstruction via nonlocal means induced prior.

The traditional Bayesian priors for maximum a posteriori (MAP) reconstruction methods usually incorporate local neighborhood interactions that penalize large deviations in parameter estimates for adjacent pixels; therefore, only local pixel differences are utilized. This limits their abilities of penalizing the image roughness. To achieve high-quality PET image reconstruction, this study investigates a MAP reconstruction strategy by incorporating a nonlocal means induced (NLMi) prior (NLMi-MAP) which enables utilizing global similarity information of image. The present NLMi prior approximates the derivative of Gibbs energy function by an NLM filtering process. Specially, the NLMi prior is obtained by subtracting the current image estimation from its NLM filtered version and feeding the residual error back to the reconstruction filter to yield the new image estimation. We tested the present NLMi-MAP method with simulated and real PET datasets. Comparison studies with conventional filtered backprojection (FBP) and a few iterative reconstruction methods clearly demonstrate that the present NLMi-MAP method performs better in lowering noise, preserving image edge and in higher signal to noise ratio (SNR). Extensive experimental results show that the NLMi-MAP method outperforms the existing methods in terms of cross profile, noise reduction, SNR, root mean square error (RMSE) and correlation coefficient (CORR).

Adaptive Edge-Preserving Image Denoising using Arbitrarily Shaped Local Windows in Wavelet Domain

denoising is a well explored topic in the field of image processing. A denoising algorithm is designed to suppress the noise while preserving as many image structures and details as possible. This paper presents a novel technique for edge- preserving image denoising using wavelet transforms. The multi-level decomposition of the noisy image is carried out to transform the data into the wavelet domain. An adaptive thresholding scheme which employs arbitrary shaped local windows and is based on edge strength is used to effectively reduce noise while preserving significant features of the original image. The experimental results, compared to other approaches, prove that the proposed method is suitable for various image types corrupted by Gaussian noise. Keywordstransform; arbitrary shaped window; region-based approach; noise reduction; edge-preservation.

Least-squares images for edge-preserving smoothing

In this paper, we propose least-squares images (LS-images) as a basis for a novel edge-preserving image smoothing method. The LS-image requires the value of each pixel to be a convex linear combination of its neighbors, i.e., to have zero Laplacian, and to approximate the original image in a least-squares sense. The edge-preserving property inherits from the edge-aware weights for constructing the linear combination. Experimental results demonstrate that the proposed method achieves high quality results compared to previous state-of-the-art works. We also show diverse applications of LS-images, such as detail manipulation, edge enhancement, and clip-art JPEG artifact removal.

Edge-preserving image decomposition via joint weighted least squares

Recent years have witnessed the emergence of image decomposition techniques which effectively separate an image into a piecewise smooth base layer and several residual detail layers. However, the intricacy of detail patterns in some cases may result in side-effects including remnant textures, wrongly-smoothed edges, and distorted appearance. We introduce a new way to construct an edge-preserving image decomposition with properties of detail smoothing, edge retention, and shape fitting. Our method has three main steps: suppressing high-contrast details via a windowed variation similarity measure, detecting salient edges to produce an edge-guided image, and fitting the original shape using a weighted least squares framework. Experimental results indicate that the proposed approach can appropriately smooth non-edge regions even when textures and structures are similar in scale. The effectiveness of our approach is demonstrated in the contexts of detail manipulation, HDR tone mapping, and image abstraction.

Interactive image filtering for level-of-abstraction texturing of virtual 3D scenes

Texture mapping is a key technology in computer graphics. For the visual design of 3D scenes, in particular, effective texturing depends significantly on how important contents are expressed, e.g., by preserving global salient structures, and how their depiction is cognitively processed by the user in an application context. Edge-preserving image filtering is one key approach to address these concerns. Much research has focused on applying image filters in a post-process stage to generate artistically stylized depictions. However, these approaches generally do not preserve depth cues, which are important for the perception of 3D visualization (e.g., texture gradient). To this end, filtering is required that processes texture data coherently with respect to linear perspective and spatial relationships. In this work, we present an approach for texturing 3D scenes with perspective coherence by arbitrary image filters. We propose decoupled deferred texturing with (1) caching strategies to interactively perform image filtering prior to texture mapping and (2) for each mipmap level separately to enable a progressive level of abstraction, using (3) direct interaction interfaces to parameterize the visualization according to spatial, semantic, and thematic data. We demonstrate the potentials of our method by several applications using touch or natural language inputs to serve the different interests of users in specific information, including illustrative visualization, focus+context visualization, geometric detail removal, and semantic depth of field. The approach supports frame-to-frame coherence, order-independent transparency, multitexturing, and content-based filtering. In addition, it seamlessly integrates into real-time rendering pipelines and is extensible for custom interaction techniques.

Image reconstruction from incomplete convolution data via total variation regularization

Variational models with Total Variation (TV) regularization have long been known to preserve image edges and produce high quality reconstruction. On the other hand, recent theory on compressive sensing has shown that it is feasible to accurately reconstruct images from a few linear measurements via TV regularization. However, in general TV models are difficult to solve due to the nondifferentiability and the universal coupling of variables. In this paper, we propose the use of alternating direction method for image reconstruction from highly incomplete convolution data, where an image is reconstructed as a minimizer of an energy function that sums a TV term for image regularity and a least squares term for data fitting. Our algorithm, called RecPK, takes advantage of problem structures and has an extremely low per-iteration cost. To demonstrate the efficiency of RecPK, we compare it with TwIST, a state-of-the-art algorithm for minimizing TV models. Moreover, we also demonstrate the usefulness of RecPK in image zooming.

Edge-preserving image denoising via group coordinate descent on the GPU.

Image denoising is a fundamental operation in image processing, and its applications range from the direct (photographic enhancement) to the technical (as a subproblem in image reconstruction algorithms). In many applications, the number of pixels has continued to grow, while the serial execution speed of computational hardware has begun to stall. New image processing algorithms must exploit the power offered by massively parallel architectures like graphics processing units (GPUs). This paper describes a family of image denoising algorithms well-suited to the GPU. The algorithms iteratively perform a set of independent, parallel 1D pixel-update subproblems. To match GPU memory limitations, they perform these pixel updates in-place and only store the noisy data, denoised image, and problem parameters. The algorithms can handle a wide range of edge-preserving roughness penalties, including differentiable convex penalties and anisotropic total variation. Both algorithms use the majorize-minimize framework to solve the 1D pixel update subproblem. Results from a large 2D image denoising problem and a 3D medical imaging denoising problem demonstrate that the proposed algorithms converge rapidly in terms of both iteration and run-time.

The guided bilateral filter: when the joint/cross bilateral filter becomes robust.

The bilateral filter and its variants, such as the joint/cross bilateral filter, are well-known edge-preserving image smoothing tools used in many applications. The reason of this success is its simple definition and the possibility of many adaptations. The bilateral filter is known to be related to robust estimation. This link is lost by the ad hoc introduction of the guide image in the joint/cross bilateral filter. We here propose a new way to derive the joint/cross bilateral filter as a particular case of a more generic filter, which we name the guided bilateral filter. This new filter is iterative, generic, inherits the robustness properties of the robust bilateral filter, and uses a guide image. The link with robust estimation allows us to relate the filter parameters with the statistics of input images. A scheme based on graduated nonconvexity is proposed, which allows converging to an interesting local minimum even when the cost function is nonconvex. With this scheme, the guided bilateral filter can handle non-Gaussian noise on the image to be filtered. A complementary scheme is also proposed to handle non-Gaussian noise on the guide image even if both are strongly correlated. This allows the guided bilateral filter to handle situations with more noise than the joint/cross bilateral filter can work with and leads to high peak signal-to-noise ratio values as shown experimentally.

Fast global image smoothing based on weighted least squares.

This paper presents an efficient technique for performing a spatially inhomogeneous edge-preserving image smoothing, called fast global smoother. Focusing on sparse Laplacian matrices consisting of a data term and a prior term (typically defined using four or eight neighbors for 2D image), our approach efficiently solves such global objective functions. In particular, we approximate the solution of the memory-and computation-intensive large linear system, defined over a d-dimensional spatial domain, by solving a sequence of 1D subsystems. Our separable implementation enables applying a linear-time tridiagonal matrix algorithm to solve d three-point Laplacian matrices iteratively. Our approach combines the best of two paradigms, i.e., efficient edge-preserving filters and optimization-based smoothing. Our method has a comparable runtime to the fast edge-preserving filters, but its global optimization formulation overcomes many limitations of the local filtering approaches. Our method also achieves high-quality results as the state-of-the-art optimization-based techniques, but runs ∼10-30 times faster. Besides, considering the flexibility in defining an objective function, we further propose generalized fast algorithms that perform Lγ norm smoothing (0 < γ < 2) and support an aggregated (robust) data term for handling imprecise data constraints. We demonstrate the effectiveness and efficiency of our techniques in a range of image processing and computer graphics applications.

Edge-preserving image compression using adaptive lifting wavelet transform

In this paper, a novel 2-D adaptive lifting wavelet transform is presented. The proposed algorithm is designed to further reduce the high-frequency energy of wavelet transform, improve the image compression efficiency and preserve the edge or texture of original images more effectively. In this paper, a new optional direction set, covering the surrounding integer pixels and sub-pixels, is designed. Hence, our algorithm adapts far better to the image orientation features in local image blocks. To obtain the computationally efficient and coding performance, the complete processes of 2-D adaptive lifting wavelet transform is introduced and implemented. Compared with the traditional lifting-based wavelet transform, the adaptive directional lifting and the direction-adaptive discrete wavelet transform, the new structure reduces the high-frequency wavelet coefficients more effectively, and the texture structures of the reconstructed images are more refined and clear than that of the other methods. The peak signal-to-noise ratio and the subjective quality of the reconstructed images are significantly improved.

LAPB: Locally adaptive patch-based wavelet domain edge-preserving image denoising

Image denoising is one of the most diversified research areas in the field of image processing and computer vision. It is highly desirable for a denoising technique to preserve important image features, such as edges, corners and other sharp structures of an image, after denoising. Wavelet transforms show excellent proficiency in providing efficient edge-preserving image denoising, due to their capability of suppressing noisy signals from an image. This paper presents a novel edge-preserving image denoising technique based on wavelet transforms. The multi-level decomposition of the noisy image is carried out to transform the data into the wavelet domain. A locally adaptive patch-based (LAPB) thresholding scheme is used to effectively reduce noise while preserving relevant features of the original image. Experimental results on benchmark test images demonstrate that the proposed method achieves competitive denoising performance in comparison to various state-of-the-art algorithms.

An edge-preserving adaptive image denoising

Based on nonsubsampled shearlet transform (NSST) and fuzzy support vector machines (FSVMs), we present a new denoising approach that can effectively suppress noise from an image while keeping its features intact. The noisy image is firstly decomposed into different subbands of frequency and orientation responses using NSST. The NSST detail coefficients are then divided into edge/texture-related coefficients and noise-related ones by FSVMs classifier. And finally the detail subbands of NSST coefficients are denoised by using the adaptive Bayesian threshold. Extensive experimental results demonstrate that our approach is competitive relative to many state-of-the-art denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.

Speckle Suppressing Improved Oriented Speckle Reducing Anisotropic Diffusion (IOSRAD) Filter for Medical Ultrasound Images

— Ultrasound imaging is the most commonly used imaging system in medical field. Main problem related to this imaging technique is introduction of speckle noise, thus making the image unclear. The success of ultrasonic examination depends on the image quality which is usually retarded due to speckle noise. There have been several techniques for effective suppression of speckle noise present in ultrasound images. The filtering techniques considered include anisotropic diffusion, wavelet de-noising, and local statistics. Comparison of the filters is based on their application of objective quality metrics, which quantifies the preservation of image edges, overall image distortion, and improvement in image contrast. The computational analysis quantifies the number of operations required for each speckle reduction method. A speed-accuracy analysis of various methods for anisotropic diffusion is included. It is concluded that the optimal method is the OSRAD (Oriented Speckle Reducing Anisotropic Diffusion) filter. The proposed approach technique deals with an improved OSRAD filter which gives an efficient result other than the previous filters by analysing the quality metrics.

A survey of edge-preserving image denoising methods

Reducing noise has always been one of the standard problems of the image analysis and processing community. Often though, at the same time as reducing the noise in a signal, it is important to pres...

An adaptive edge-preserving image denoising technique using tetrolet transforms

Denoising has always been one of the standard problems in image processing. It is always desirable to preserve important features, such as edges, corners and other sharp structures, during denoisin...

Detection of quiescent cardiac phases in echocardiography data using nonlinear filtering and boundary detection techniques.

We describe an algorithm to detect cardiac quiescence within a heartbeat using nonlinear filtering and boundary detection techniques in echocardiography images. The motivation for detection of these quiescent phases is to provide improved cardiac gating to obtain motion-artifact-free images of the heart at cardiac computed tomography (CT). Currently, cardiac gating is provided through electrocardiography (ECG), which does not provide information about the instantaneous mechanical state of the heart. Our goal is to test if information about the actual mechanical motion of the heart obtained from B-mode echocardiographic data could potentially be used for gating purposes. The nonlinear filtering algorithm presented involves anisotropic diffusion to smoothen the homogeneous regions of the B-mode images while preserving image edges that represent myocardial boundaries. Following this, we detect the boundary of a particular region of interest (ROI) using a thresholding step. The positional changes of this ROI are then observed for quiescent phases over multiple cardiac cycles using the ECG's R-R interval. In a pilot study, seven subjects were imaged in the apical, four-chamber view, and quiescence of the interventricular septum was primarily observed in the diastolic region of the ECG signal. However, the position and length of quiescence vary across multiple heartbeats for the same individual and for different individuals as well. The center of quiescence for the seven patients ranged from 51 to 84% and did not show a trend with heart rates, which ranged from 54 to 83 beats per minute. The gating intervals based on such analysis of echocardiographic signals could potentially optimize cardiac CT gating.

Catenary image enhancement using wavelet-based contourlet transform with cycle translation

The image quality is extremely important in the catenary fault diagnosis based on the image processing. In order to improve image visual effects, this paper proposes a catenary image enhancement method based on the wavelet-based contourlet transform (WBCT) with cycle translation. First, the Laplacian pyramid (LP) transform is replaced by the wavelet transform (WT) for reducing the redundancy of the contourlet transform (CT). Meanwhile, combine the WBCT with cycle translation to overcome the problem of WBCT without the translational invariance. Finally, the adaptive enhancement function is chosen to enhance the catenary images. Experimental results show that the proposed method has the advantages of preserving image edge details and texture. In terms of visual effects, signal-to-noise ratio (SNR) and edge preserving index (EPI), the proposed algorithm outperforms the traditional methods.

Image denoising in extended Shearlet domain using hidden Markov tree models

Denoising of images is one of the most basic tasks of image processing. It is a challenging work to design an edge-preserving image denoising scheme. Extended discrete Shearlet transform (extended DST) is an effective multi-scale and multi-direction analysis method; it not only can exactly compute the Shearlet coefficients based on a multiresolution analysis, but also can provide nearly optimal approximation for a piecewise smooth function. In this paper, a new image denoising approach in extended Shearlet domain using hidden Markov tree (HMT) model is proposed. Firstly, the joint statistics and mutual information of the extended DST coefficients are studied. Then, the extended DST coefficients are modeled using an HMT model with Gaussian mixtures, which can effectively capture the intra-scale and inter-scale dependencies. Finally, the extended Shearlet HMT model is applied to image denoising. Extensive experimental results demonstrate that our extended Shearlet HMT denoising method can obtain better performances in terms of both subjective and objective evaluations than other state-of-the-art HMT denoising techniques. Especially, the proposed method can preserve edges very well while removing noise.