scholarly journals Adaptive Sparse Norm and Nonlocal Total Variation Methods for Image Smoothing

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Qiegen Liu ◽  
Biao Xiong ◽  
Minghui Zhang
Keyword(s):  
Total Variation ◽  
Image Smoothing ◽  
Penalty Term ◽  
Feature Structure ◽  
Nonlocal Total Variation ◽  
Data Fidelity ◽  
Spatially Varying ◽  
Penalty Weight ◽  
Structure Patterns

In computer vision and graphics, it is challenging to decompose various texture/structure patterns from input images. It is well recognized that how edges are defined and how this prior information guides smoothing are two keys in determining the quality of image smoothing. While many different approaches have been reported in the literature, sparse norm and nonlocal schemes are two promising tools. In this study, by integrating a texture measure as the spatially varying data-fidelity/smooth-penalty weight into the sparse norm and nonlocal total variation models, two new methods are presented for feature/structure-preserving filtering. The first one is a generalized relative total variation (i.e., GRTV) method, which improves the contrast-preserving and edge stiffness-enhancing capabilities of the RTV by extending the range of the penalty function’s norm from 1 to [0, 1]. The other one is a nonlocal version of generalized RTV (i.e., NLGRTV) for which the key idea is to use a modified texture-measure as spatially varying penalty weight and to replace the local candidate pixels with the nonlocal set in the smooth-penalty term. It is shown that NLGRTV substantially improves the performance of decomposition for regions with faint pixel-boundary.

A new total variation model for restoring blurred and speckle noisy images

2017 ◽  
Vol 15 (02) ◽  
pp. 1750009 ◽  
Author(s):  
Jian Lu ◽  
Yupeng Chen ◽  
Yuru Zou ◽  
Lixin Shen
Keyword(s):  
Total Variation ◽  
Speckle Noise ◽  
Variational Model ◽  
Total Variation Regularization ◽  
Penalty Term ◽  
Proposed Model ◽  
Total Variation Model ◽  
Data Fidelity ◽  
Blurred Images ◽  
Uniqueness Of A Solution

In coherent imaging systems, such as the synthetic aperture radar (SAR), the observed images are affected by multiplicative speckle noise. This paper proposes a new variational model based on I-divergence for restoring blurred images with speckle noise. The model minimizes the sum of an I-divergence data fidelity term, a new quadratic penalty term based on the statistical property of the noise and the total-variation regularization term. The existence and uniqueness of a solution of the proposed model with some other characteristics are analyzed. Furthermore, an iterative algorithm is introduced to solve the proposed variational model. Our numerical experiments indicate that the proposed method performs favorably.

scholarly journals Image Compression Approach using Segmentation and Total Variation Regularization

2021 ◽  
Vol 15 ◽  
pp. 43-47
Author(s):  
Ahmad Shahin ◽  
Walid Moudani ◽  
Fadi Chakik
Keyword(s):  
Image Compression ◽  
Total Variation ◽  
Image Data ◽  
Smart Devices ◽  
Multimedia Content ◽  
Total Variation Regularization ◽  
Second Stage ◽  
Tv Regularization ◽  
Rof Model

In this paper we present a hybrid model for image compression based on segmentation and total variation regularization. The main motivation behind our approach is to offer decode image with immediate access to objects/features of interest. We are targeting high quality decoded image in order to be useful on smart devices, for analysis purpose, as well as for multimedia content-based description standards. The image is approximated as a set of uniform regions: The technique will assign well-defined members to homogenous regions in order to achieve image segmentation. The Adaptive fuzzy c-means (AFcM) is a guide to cluster image data. A second stage coding is applied using entropy coding to remove the whole image entropy redundancy. In the decompression phase, the reverse process is applied in which the decoded image suffers from missing details due to the coarse segmentation. For this reason, we suggest the application of total variation (TV) regularization, such as the Rudin-Osher-Fatemi (ROF) model, to enhance the quality of the coded image. Our experimental results had shown that ROF may increase the PSNR and hence offer better quality for a set of benchmark grayscale images.

Impact of total variation regularized expectation maximization reconstruction on the image quality of 68Ga-PSMA PET: a phantom and patient study

2021 ◽  
pp. 20201356
Author(s):  
Feng-Jiao Yang ◽  
Shu-Yue Ai ◽  
Runze Wu ◽  
Yang Lv ◽  
Hui-Fang Xie ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Image Quality ◽  
Total Variation ◽  
Expectation Maximization ◽  
Image Noise ◽  
Psma Pet ◽  
Pet Ct ◽  
Digital Pet ◽  
The Impact

Objectives: To investigate the impact of total variation regularized expectation maximization (TVREM) reconstruction on the image quality of 68Ga-PSMA-11 PET/CT using phantom and patient data. Methods: Images of a phantom with small hot sphere inserts and 20 prostate cancer patients were acquired with a digital PET/CT using list-mode and reconstructed with ordered subset expectation maximization (OSEM) and TVREM with seven penalisation factors between 0.01 and 0.42 for 2 and 3 minutes-per-bed (m/b) acquisition. The contrast recovery (CR) and background variability (BV) of the phantom, image noise of the liver, and SUVmax of the lesions were measured. Qualitative image quality was scored by two radiologists using a 5-point scale (1-poor, 5-excellent). Results: The performance of CR, BV, and image noise, and the gain of SUVmax was higher for TVREM 2 m/b groups with the penalization of 0.07 to 0.28 compared to OSEM 3 m/b group (all p < 0.05). The image noise of OSEM 3 m/b group was equivalent to TVREM 2 and 3 m/b groups with a penalization of 0.14 and 0.07, while lesions’ SUVmax increased 15 and 20%. The highest qualitative score was attained at the penalization of 0.21 (3.30 ± 0.66) for TVREM 2 m/b groups and the penalization 0.14 (3.80 ± 0.41) for 3 m/b group that equal to or greater than OSEM 3 m/b group (2.90 ± 0.45, p = 0.2 and p < 0.001). Conclusions: TVREM improves lesion contrast and reduces image noise, which allows shorter acquisition with preserved image quality for PSMA PET/CT. Advances in knowledge: TVREM reconstruction with optimized penalization factors can generate higher quality PSMA-PET images for prostate cancer diagnosis.

The nonlocal total variation flow

2010 ◽  
pp. 163-189 ◽  
Author(s):  
Fuensanta Andreu-Vaillo ◽  
José Mazón ◽  
Julio Rossi ◽  
J. Julián Toledo-Melero
Keyword(s):  
Total Variation ◽  
Total Variation Flow ◽  
Nonlocal Total Variation

scholarly journals A COMPARISON OF HAZE REMOVAL ALGORITHMS AND THEIR IMPACTS ON CLASSIFICATION ACCURACY FOR LANDSAT IMAGERY

2017 ◽  
Vol 23 (1) ◽  
pp. 55-71 ◽  
Author(s):  
Yang Xiao ◽  
Zhiyun Ouyang ◽  
Zhiming Zhang ◽  
Chaofan Xian
Keyword(s):  
Classification Accuracy ◽  
Near Infrared ◽  
Landsat Imagery ◽  
Spectral Response ◽  
Landsat Images ◽  
Haze Removal ◽  
Reference Images ◽  
The Difference ◽  
Spatially Varying

The quality of Landsat images in humid areas is considerably degraded by haze in terms of their spectral response pattern, which limits the possibility of their application in using visible and near-infrared bands. A variety of haze removal algorithms have been proposed to correct these unsatisfactory illumination effects caused by the haze contamination. The purpose of this study was to illustrate the difference of two major algorithms (the improved homomorphic filtering (HF) and the virtual cloud point (VCP)) for their effectiveness in solving spatially varying haze contamination, and to evaluate the impacts of haze removal on land cover classification. A case study with exploiting large quantities of Landsat TM images and climates (clear and haze) in the most humid areas in China proved that these haze removal algorithms both perform well in processing Landsat images contaminated by haze. The outcome of the application of VCP appears to be more similar to the reference images compared to HF. Moreover, the Landsat image with VCP haze removal can improve the classification accuracy effectively in comparison to that without haze removal, especially in the cloudy contaminated area

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