Search

Purpose: To reduce the ringing artifacts of the motion‐resolved images in free‐breathing dynamic pulmonary MRI. Methods: A golden‐step based interleaving (GSI) technique was proposed to reduce ringing artifacts induced by diaphragm drifting. The pulmonary MRI data were acquired using a superior–inferior navigated 3D radial UTE sequence in an interleaved manner during free breathing. Successive interleaves were acquired in an incoherent fashion along the polar direction. Four‐dimensional images were reconstructed from the motion‐resolved k‐space data obtained by retrospectively binning. The reconstruction algorithms included standard nonuniform fast Fourier transform (NUFFT), Voronoi‐density‐compensated NUFFT, extra‐dimensional UTE, and motion‐state weighted motion‐compensation reconstruction. The proposed interleaving technique was compared with a conventional sequential interleaving (SeqI) technique on a phantom and eight subjects. Results: The quantified ringing artifacts level in the motion‐resolved image is positively correlated with the quantified nonuniformity level of the corresponding k‐space. The nonuniformity levels of the end‐expiratory and end‐inspiratory k‐space binned from GSI data (0.34 ± 0.07, 0.33 ± 0.05) are significantly lower with statistical significance (p < 0.05) than that binned from SeqI data (0.44 ± 0.11, 0.42 ± 0.12). Ringing artifacts are substantially reduced in the dynamic images of eight subjects acquired using the proposed technique in comparison with that acquired using the conventional SeqI technique. Conclusion: Ringing artifacts in the motion‐resolved images induced by diaphragm drifting can be reduced using the proposed GSI technique for free‐breathing dynamic pulmonary MRI. This technique has the potential to reduce ringing artifacts in free‐breathing liver and kidney MRI based on full‐echo interleaved 3D radial acquisition. [ABSTRACT FROM AUTHOR]

Blind image deblurring tries to restore a blurred image to a clear image without the blurring kernel known in advance, which is widely required in applications such as computer vision and medical image processing. With regard to this, the key issues here are to accurately estimate the blurring kernel for deconvolution of a blurred image, and avoid the ringing artifacts in the restored image, which are both related to high-quality detection of edge information in the blurred image. Though much endeavor has been made, it is still difficult to extract edge information well in blurred images and lacks investigation how edge information causes ringing artifacts. In this paper, we make a study on this and develop novel measures to optimize edge extraction and determine suitable width and weights for the extracted edges for reinforcing their use in deblurring, by which image deblurring can be improved with ringing artifacts considerably suppressed. Experimental results demonstrate our improvements over the existing methods. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Image & Graphics is the property of Editorial Office of Journal of Image & Graphics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, we present a simple yet effective image deblurring method to produce ringing-free deblurred images. Our work is inspired by the observation that large-scale deblurring ringing artifacts are measurable through a multi-resolution pyramid of low-pass filtering of the blurred-deblurred image pair. We propose to model such a quantification as a convex cost function and minimize it directly in the deblurring process in order to reduce ringing regardless of its cause. An efficient primal-dual algorithm is proposed as a solution to this optimization problem. Since the regularization is more biased toward ringing patterns, the details of the reconstructed image are prevented from over-smoothing. An inevitable source of ringing is sensor saturation which can be detected costlessly contrary to most other sources of ringing. However, dealing with the saturation effect in deblurring introduces a non-linear operator in optimization problem. In this paper, we also introduce a linear approximation as a solution to handling saturation in the proposed deblurring method. As a result of these steps, we significantly enhance the quality of the deblurred images. Experimental results and quantitative evaluations demonstrate that the proposed method performs favorably against state-of-the-art image deblurring methods. [ABSTRACT FROM AUTHOR]

In this paper, we have proposed a blind motion deblurring algorithm that comprises the estimation of the motion blur parameters (length and angle) in a modified cepstrum domain with a blind no-reference image spatial quality evaluator (BRISQUE) used for the tuning of point spread function (PSF) parameters. Ringing artifacts are generated during the deblurring process. In this paper, the modified R–L (Richardson–Lucy) algorithm with weight calculation based on graphcut is presented to obtain good estimates of the unblurred image with ringing reduction. The method involves the selection of different weights for edges and smooth regions such that the ringing effect over R–L iterations can be reduced. A newly proposed method has been tested on various natural images with a motion blur of different length and degrees. A comparison with state-of-the-art methods proves that the proposed technique achieved better results in terms of different quality measures such as SSIM, FSIM and PSNR and can be greatly beneficial for deblurring purpose. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Iterative methods are typically utilized for blind image restoration (BIR); however, they are relatively slow, uncertain, and occasionally ill-behaved. This study presents a non-iterative algorithm to estimate the parameters of point spread functions (PSFs), particularly, Class G. We propose a curve model to approximate the normalized spectrum amplitude of the original image in accordance with the decay law of the natural image spectrum. The blur PSF is estimated by comparing the original image spectrum with the degraded one. Then, the image is restored by applying the estimated PSF and the Wiener filter. Experimental results demonstrate that the proposed algorithm can obtain a more accurate PSF and reduce ringing artifacts compared with the existing algorithms. The quality of the restored images is enhanced significantly. [ABSTRACT FROM AUTHOR]

Since blur kernel estimation is an ill-posed problem, it is essential that it be constrained by parametric image priors. However, the previous normalized sparsity measure alters the kernel structure during estimation. To address the problem of single-image blur kernel estimation, a local smoothness prior is introduced to the normalized sparsity model to constrain the blurred image gradient to be similar to the unblurred one. Moreover, based on the inequality constraints, a kernel optimization algorithm is proposed to weaken the noise. Experimental results show that the proposed method is robust against noise and is able to estimate a stable blur kernel. It outperforms other state-of-the-art methods on both synthetic and real data. [ABSTRACT FROM AUTHOR]

In contrast with traditional extended depth-of-field approaches, we propose a depth-based deconvolution technique that realizes the depth-variant nature of the point spread function of an ordinary fixed-focus camera. The developed technique brings a single blurred image to focus at different depth planes which can be stitched together based on a depth map to output a full-focus image. Strategies to suppress the deconvolution's ringing artifacts are implemented on three levels: block tiling to eliminate boundary artifacts, reference maps to reduce ringing initiated by sharp edges, and depth-based masking to mitigate artifacts raised by neighboring depth-transition surfaces. The performance is validated numerically for planar and multidepth objects. [ABSTRACT FROM AUTHOR]

Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform (SWT) algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolution ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT subbands for analysis, and a new multiscale adaptive anisotropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail. [ABSTRACT FROM AUTHOR]

To solve the image deblurring problem, most of the recently proposed nonblind deblurring approaches focus on regularizing the solutions by adding image priors, such as sparse prior, piece-wise smooth prior (totalvariation prior), and so on. This paper, however, presents a new term, which can be viewed as a supplement to the data fidelity term, to improve the nonblind deblurring result. The new term is based on the relative error in frequency domain (REFD) and can suppress the ringing artifacts and preserve the image details. We analyze the properties of REFD for image deconvolution; then, a nonblind image deconvolution algorithm is proposed via introducing the REFD term into the image deconvolution model. The experimental results demonstrate the performance and efficacy of our proposed algorithm when compared to some other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

It is well known that at low bit rates, a block-based discrete cosine transform compressed image or video can exhibit visually annoying blocking and ringing artifacts. Low-pass filters are very effective in reducing the blocking artifacts in smooth areas. However, it is difficult to achieve a satisfactory result for ringing artifact removal using only an adaptive filtering scheme. This paper presents a neural network-based deblocking method that is effective on various types of images. The first step of this scheme is block classification that identifies each 8 × 8 block as one of the three types: PLAIN, EDGE or TEXTURE, based on its statistical characteristics. The next step is the reduction in the blocking and ringing artifacts by applying three trained layered neural networks to three different types of image areas. Comparing this method with other algorithms, the simulation results clearly show that the proposed algorithm is very powerful in effectively reducing both blocking and ringing artifacts while preserving the true edge and textural information and thus significantly improving the visual quality of the blocking images or videos. [ABSTRACT FROM AUTHOR]

This paper proposes a new ringing-artifact reduction method for image resizing in a block discrete cosine transform (DCT) domain. The proposed method reduces ringing artifacts without further blurring, whereas previous approaches must find a compromise between blurring and ringing artifacts. The proposed method consists of DCT-domain filtering and image-domain post-processing, which reduces ripples on smooth regions as well as overshoot near strong edges. By generating a mask map of the overshoot regions, we combine a ripple-reduced image and an overshoot-reduced image according to the mask map in the image domain to obtain a ringing-artifact reduced image. The experimental results show that the proposed method is computationally faster and produces visually finer images than previous ringing-artifact reduction approaches. [ABSTRACT FROM AUTHOR]

Purpose: Studies have shown that Monte Carlo-based reconstruction could effectively improve the image quality of positron emission tomography. The authors have previously used a Gaussian rotator-based algorithm to efficiently reduce the computational cost for system matrix (SM) calculation and to meet the large memory requirements for SM storage. However, pronounced ringing artifacts were observed in the reconstructed image. In this article, the authors investigated and characterized these artifacts. Methods: The authors proposed an “ideal” rotator and used it as a baseline in the artifacts evaluation. This ideal rotator produces perfectly rotated images. The Gaussian rotator method was evaluated by a full system model and a partial system model without positron range and acolinearity, which could be compensated for by the blurring of the Gaussian rotator for 18F studies. Noiseless data, Monte Carlo simulation data, as well as acquired experimental data were used to quantitatively characterize the behavior of the artifacts. Results: The study of the noiseless data indicated that the artifacts were mainly attributed to the rotator, which further blurred the simulated system responses. The simulation study suggested that the artifacts become less pronounced and not quantitatively significant in practice. This result is consistent with the experimental data study. Better contrast recovery was achieved with an overcompensated system model. Traditionally, an undercompensated system model was preferred to avoid artifacts. The authors’ studies suggest that the Gaussian rotator with the full system model yields the best image quality among the evaluated methods with considerably reduced quantitative error and quantitatively insignificant artifacts in practice. Conclusions: The authors’ investigation indicated that a moderately overcompensated system model (about 2 mm FWHM in this study) yielded better contrast recovery and quantitatively insignificant artifacts in practice. [ABSTRACT FROM AUTHOR]

We present a new algorithm for removing motion blur from a single image. Our method computes a deblurred image using a unified probabilistic model of both blur kernel estimation and unblurred image restoration. We present an analysis of the causes of common artifacts found in current deblurring methods, and then introduce several novel terms within this probabilistic model that are inspired by our analysis. These terms include a model of the spatial randomness of noise in the blurred image, as well a new local smoothness prior that reduces ringing artifacts by constraining contrast in the unblurred image wherever the blurred image exhibits low contrast. Finally, we describe an efficient optimization scheme that alternates between blur kernel estimation and unblurred image restoration until convergence. As a result of these steps, we are able to produce high quality deblurred results in low computation time. We are even able to produce results of comparable quality to techniques that re- quire additional input images beyond a single blurry photograph, and to methods that require additional hardware. [ABSTRACT FROM AUTHOR]

This paper proposes a new adaptive post-filtering algorithm to remove coding artifacts in block-based video coder. The proposed method concentrates on blocking and ringing artifacts removal. For de-blocking, the blocking strength is identified to determine the filtering range, and the maximum quantization parameter of the image is used to adapt the 1D fuzzy filter. For de-ringing, besides the edge detection, a complementary ringing detection method is proposed to locate the neglected ringing blocks, and the gradient threshold is adopted to adjust the parameter of 2D fuzzy filter. Experiments are performed on the MPEG-4 sequences. Compared with other methods, the proposed one achieves better detail preservation and artifacts removal performance with lower computational cost. [ABSTRACT FROM AUTHOR]

Images are often coded using block-based discrete cosine transform (DCT), where blocking and ringing artifacts are the most common visual distortion. In this letter, a fast algorithm is proposed to alleviate the said artifacts in the DCT domain. The new concept is to decompose a row or column image vector to a gradually changed signal and a fast variational signal, which correspond to low-frequency (LF) and high-frequency (HF) DCT subbands, respectively. Blocking artifacts between adjacent LF blocks are suppressed by smoothing LF components and discarding invalid HF ones, and ringing artifacts inside HF vectors are reduced by a simplified bilateral filter. With such a process, edges are preserved while blockiness and ringing are alleviated. Analytic and experimental results confirm the robustness and computational efficiency of the proposed method. [ABSTRACT FROM AUTHOR]