Noisy Blocks Research Articles

Multi-resolution dictionary-based sparse coding method for interferometric phase image denoising

Interferometric synthetic aperture radar (InSAR) is widely used in acquisition of digital elevation data as well as the settlement of Earth surface. Researching on the processing of space-born InSAR is of great importance in improving the accuracy of sensing data. The phase denoising method based on multi-resolution dictionary sparse coding (SC) is proposed. First, noisy blocks are extracted from the complex valued interferometric image. Then, the dictionary is trained by online method, which uses a portion of elements in the training set to update the dictionary column by column. And the trained dictionary is used to sparsely represent the noisy blocks by greedy method to denoise the image patches with different resolutions. The whole image of a given resolution is formed by combining the patches in the same resolution together. Finally, the denoising results of different resolutions are merged based on hypothesis testing and the phase estimations according to each pixels are computed. The experiments prove that the proposed denoising method based on multi-resolution SC achieves higher peak signal to noise ratio than traditional SC method.

Tile selection method based on error minimization for photomosaic image creation

Photomosaic images are composite images composed of many small images called tiles. In its overall visual effect, a photomosaic image is similar to the target image, and photomosaics are also called “montage art”. Noisy blocks and the loss of local information are the major obstacles in most methods or programs that create photomosaic images. To solve these problems and generate a photomosaic image in this study, we propose a tile selection method based on error minimization. A photomosaic image can be generated by partitioning the target image in a rectangular pattern, selecting appropriate tile images, and then adding them with a weight coefficient. Based on the principles of montage art, the quality of the generated photomosaic image can be evaluated by both global and local error. Under the proposed framework, via an error function analysis, the results show that selecting a tile image using a global minimum distance minimizes both the global error and the local error simultaneously. Moreover, the weight coefficient of the image superposition can be used to adjust the ratio of the global and local errors. Finally, to verify the proposed method, we built a new photomosaic creation dataset during this study. The experimental results show that the proposed method achieves a low mean absolute error and that the generated photomosaic images have a more artistic effect than do the existing approaches.

Low-Frequency Noise Suppression in Desert Seismic Data Based on an Improved Weighted Nuclear Norm Minimization Algorithm

Noise suppression is a crucial step before seismic data analysis. The noise in desert areas has the characteristics of nonstationary, non-Gaussian, and low-frequency, which makes some traditional methods cannot suppress the noise well. The weighted nuclear norm minimization (WNNM), one of the most effective methods to suppress noise in all low-rank matrix approximation methods, assigns different weights to different singular values. The good denoising performance of the WNNM is based on the accuracy of block matching, and the theoretical basis of block matching is the nonlocal self-similarity of clean signals. However, the noise will destroy the nonlocal self-similarity and greatly affect the accuracy of block matching. In this article, to reduce the influence of noise on block matching, we use a bandpass filter to process each noisy block before block-matching and use the similarity between the filtered blocks to represent the similarity between the original noisy blocks, then stack the most similar noisy blocks to construct a matrix for further denoising. Experimental results demonstrate the efficiency of the proposed method for low-frequency noise suppression.

Block-Based Noisy/Clean Classification of Images Using the Common Vector Approach

In this paper, a novel method is proposed to determine noisy blocks of an image. Three different threshold values for noisy/clean classification of the blocks of any image are determined by applying the common vector approach to the reference data set consisting of the clean samples of that image. The noise addressed in this paper is Gaussian noise with zero mean. By making a block-based noisy/clean classification of any image, it is possible to expose only the noisy blocks to the denoising process, rather than the entire image to denoising. When the first threshold value (Threshold 1) is considered for all peak signal-to-noise ratio (PSNR) values, more than 94% classification results are obtained for all 8 × 8 block-sized test images except images with 28–31 dB PSNR and more than 98.8% classification results are obtained for all 12 × 12 and 16 × 16 block-sized test images except images with 29-31 dB PSNR. Finally, popular image denoising algorithms are applied to the noisy images for comparison. It is observed that the PSNR values of noisy images are appreciably increased after the process.

Adaptive algorithm utilizing acceptance rate for eliminating noisy epochs in block-design functional near-infrared spectroscopy data: application to study in attention deficit/hyperactivity disorder children.

.Functional near-infrared spectroscopy (fNIRS) signals are prone to problems caused by motion artifacts and physiological noises. These noises unfortunately reduce the fNIRS sensitivity in detecting the evoked brain activation while increasing the risk of statistical error. In fNIRS measurements, the repetitive resting-stimulus cycle (so-called block-design analysis) is commonly adapted to increase the sample number. However, these blocks are often affected by noises. Therefore, we developed an adaptive algorithm to identify, reject, and select the noise-free and/or least noisy blocks in accordance with the preset acceptance rate. The main features of this algorithm are personalized evaluation for individual data and controlled rejection to maintain the sample number. Three typical noise criteria (sudden amplitude change, shifted baseline, and minimum intertrial correlation) were adopted. Depending on the quality of the dataset used, the algorithm may require some or all noise criteria with distinct parameters. Aiming for real applications in a pediatric study, we applied this algorithm to fNIRS datasets obtained from attention deficit/hyperactivity disorder (ADHD) children as had been studied previously. These datasets were divided for training and validation purposes. A validation process was done to examine the feasibility of the algorithm regardless of the types of datasets, including those obtained under sample population (ADHD or typical developing children), intervention (nonmedication and drug/placebo administration), and measurement (task paradigm) conditions. The algorithm was optimized so as to enhance reproducibility of previous inferences. The optimum algorithm design involved all criteria ordered sequentially (0.047 mM mm of amplitude change, of baseline slope, and range of outlier threshold for each criterion, respectively) and presented complete reproducibility in both training and validation datasets. Compared to the visual-based rejection as done in the previous studies, the algorithm achieved 71.8% rejection accuracy. This suggests that the algorithm has robustness and potential to substitute for visual artifact-detection.

Erratum: Adaptive algorithm utilizing acceptance rate for eliminating noisy epochs in block-design functional near-infrared spectroscopy data: application to study in attention deficit/hyperactivity disorder children.

[This corrects the article DOI: 10.1117/1.NPh.5.4.045001.].

COMPRESSIVE SENSING BASED SIGNAL RECOVERY WITH DIFFERENT TRANSFORMS

Compressive sensing (CS) is a new technique that give an approach to reconstruct the signal with few numbers of observations or measurements. The CS is based on L1 -norm minimizations to find the sparse solutions and it is known as basis pursuit. In this investigation, CS scheme with different transforms is proposed. The three utilized transform techniques are: Discrete Fourier Transform(DFT), Discrete Cosine Transform(DCT) and Discrete Wavelet Transform(DWT) with daubechies1(DB1) and coiflets1(coif1) basis. The proposed system is tested by employing the following signals: Blocks, Heavy Sine, ‘Bumps’ and ‘Doppler’ which cover wide range of applications. The four testing signals are represented in sparse domain using different transforms. In order to threshold the coefficients of the signals in sparse domain, the universal threshold is utilized in the case of CS with FFT and DWT whereas, the universal threshold is modified to prune the DCT coefficients. The main aim of this study is to investigate the differences among CS with DFT, CS with DCT and CS with DWT, and consequently a suitable transform domain used with CS to be selected. The comparative study is established by assessing the performance of the proposed system using Root Mean Square Error (RMSE), output SNR, and the time required to reconstruct approximated signals. Simulation results have shown that the CS with DWT outperforms the CS with FFT and DCT. CS with DWT has achieved good RMSE values about (0.0014 to 3.359e-8) even when half of the signal elements are removed. CS with FFT and DCT enhanced the noisy Blocks and Bumps signals by 3dB and 1dB respectively, while it is failed to enhance noisy Heavy Sine and Doppler signals. CS with DWT of two basis and for single decomposition level have improved the noisy Blocks, Bumps, Heavy Sine and Doppler signals by 5dB, 4dB, 3dB, and 3dB respectively.

Smoothness Measure for Image Fusion in Discrete Cosine Transform

The aim of image fusion is to generate high-quality images using information from source images. The fused image contains more information than any of the source images. Image fusion using transforms is more effective than spatial methods. Statistical measures such as mean, contrast, and variance, are used in Discrete Cosine Transform (DCT) for image fusion. In this paper, we use statistical measures, such as the smoothness of a block in the transform domain, to select appropriate blocks from multiple images to obtain a fused image. Smoothness captures important blocks in images and duly eliminates noisy blocks. Furthermore, we compare and analyze all statistical measures in the DCT domain. Experimental results establish the superiority of our proposed method over state-of-the-art techniques for image fusion.

Detection on Large Amount of Web Pages Based on a Highly Active Way of Site-Level Template

With the explosive growth of the information on the Internet, the number of web pages is also expanding rapidly. A common web page typically contains many different blocks. Unfortunately, apart from the main content blocks, it usually has such blocks as navigation bars, copyright, and advertisements, which are known as noise. Studies have shown that these noisy blocks can do harm to Web mining, search engine performance, and Web page clustering and classification. It is a significant work to effectively clean these noises up and extract main content blocks from the Web pages. In this paper, we propose a method of automatic template detection and noise elimination based on an new idea defining two sorts of templates called Style Template and Content Template relatively. There is a basic observation that, the Web pages from the same site are always similar in presentation style and contents, however, the main content blocks are always different in contents. Therefore, we design an algorithm to detect the two kind of templates from the samplings from a particular site, and extract the main content out of the noises using the template. We apply our method to template problem, and the result shows that our methods performs very well, and has a remarkable advantage on time consumption over previous methods.

Identifying Informative Web Content Blocks using Web Page Segmentation

Information Extraction has become an important task for discovering useful knowledge or information from the Web. A crawler system, which gathers the information from the Web, is one of the fundamental necessities of Information Extraction. A search engine uses a crawler to crawl and index web pages. Search engine takes into account only the informative content for indexing. In addition to informative content, web pages commonly have blocks that are not the main content blocks and are called the non-informative blocks or noise. Noise is generally illogical with the main content of the page and affects two major parameters of search engines: the precision of search and the size of index In order to improve the performance of information retrieval, cleaning of Web pages becomes critical. The main objective of proposed technique is to eliminate the non-informative content blocks from a Web Page. In the proposed technique, the extraction of informative content blocks and elimination of non informative blocks is based on the idea of Web page Segmentation. Here, a web page is divided into n blocks and the block importance is calculated for each block. The blocks with importance >=threshold are considered as important blocks and the remaining blocks are eliminated as noisy blocks. The proposed approach saves significant space and time

Effectiveness of template detection on noise reduction and websites summarization

The World Wide Web is the most rapidly growing and accessible source of information. Its popularity has been largely influenced by the wide availability of the Internet in almost every modern house and even on the go after the wide-spread of the handheld devices. Yet, pages on the Web have an additional template (we call it noisy) information that does not add value to the actual content of the page. Even worse, it can harm the effectiveness of Web mining techniques; these templates could be eliminated by preprocessing. Templates form one popular type of noise on the Internet. In this paper, we introduce Noise Detector (ND) as an effective approach for detecting and removing templates from Web pages. ND segments Web pages into semantically coherent blocks. Then it computes content and structure similarities between these blocks; a presentational noise measure is used as well. ND dynamically calculates a threshold for differentiating noisy blocks. Provided that the investigated website has a single visible template, ND can detect the template with high accuracy using two pages only. However, ND can be expanded to detect multiple templates per website, and the challenge will be to minimize the number of pages to be checked. Further, ND leads to website summarization. The conducted experiments show that ND outperforms existing approaches in space complexity, time complexity (see Section 4.6 for more details on ND’s processing time against other algorithms’), minimum requirements to produce acceptable results, and results accuracy.

Secure Bit-Plane Based Steganography for Secret Communication

A secure method for steganography is proposed. Pixel-value differencing (PVD) steganography and bit-plane complexity segmentation (BPCS) steganography have the weakness of generating blocky effects and noise in smooth areas and being detectable with steganalysis. To overcome these weaknesses, a secure bit-plane based steganography method on the spatial domain is presented, which uses a robust measure to select noisy blocks for embedding messages. A matrix embedding technique is also applied to reduce the change of cover images. Given that the statistical property of cover images is well preserved in stego-images, the proposed method is undetectable by steganalysis that uses RS analysis or histogram-based analysis. The proposed method is compared with the PVD and BPCS steganography methods. Experimental results confirm that the proposed method is secure against potential attacks.

A method of eliminating noises in Web pages by style tree model and its applications

A Web page typically contains many information blocks. Apart from the main content blocks, it usually has such blocks as navigation panels, copyright and privacy notices, and advertisements. We call these blocks the noisy blocks. The noises in Web pages can seriously harm Web data mining. To the question of climinating these noises, we intro duce a new tree structure, called Style Tree, and study an algorithm how to construct a site style tree. The Style Tree Model is employed to detect and climinate noises in any Web pages of the site. An information based measure to determine which element node is noisy is also constructed. In addition, the applications of this method are discussed in detail. Experimental results show that our noises climination technique is able to improve the mining results significantly.