scispace - formally typeset
Search or ask a question

Showing papers on "Median filter published in 2013"


Journal ArticleDOI
TL;DR: A patch-based noise level estimation algorithm that selects low-rank patches without high frequency components from a single noisy image and estimates the noise level based on the gradients of the patches and their statistics is proposed.
Abstract: Noise level is an important parameter to many image processing applications. For example, the performance of an image denoising algorithm can be much degraded due to the poor noise level estimation. Most existing denoising algorithms simply assume the noise level is known that largely prevents them from practical use. Moreover, even with the given true noise level, these denoising algorithms still cannot achieve the best performance, especially for scenes with rich texture. In this paper, we propose a patch-based noise level estimation algorithm and suggest that the noise level parameter should be tuned according to the scene complexity. Our approach includes the process of selecting low-rank patches without high frequency components from a single noisy image. The selection is based on the gradients of the patches and their statistics. Then, the noise level is estimated from the selected patches using principal component analysis. Because the true noise level does not always provide the best performance for nonblind denoising algorithms, we further tune the noise level parameter for nonblind denoising. Experiments demonstrate that both the accuracy and stability are superior to the state of the art noise level estimation algorithm for various scenes and noise levels.

381 citations


Journal ArticleDOI
TL;DR: This paper shows that the noise variance can be estimated as the smallest eigenvalue of the image block covariance matrix, which is at least 15 times faster than methods with similar accuracy, and at least two times more accurate than other methods.
Abstract: The problem of blind noise level estimation arises in many image processing applications, such as denoising, compression, and segmentation. In this paper, we propose a new noise level estimation method on the basis of principal component analysis of image blocks. We show that the noise variance can be estimated as the smallest eigenvalue of the image block covariance matrix. Compared with 13 existing methods, the proposed approach shows a good compromise between speed and accuracy. It is at least 15 times faster than methods with similar accuracy, and it is at least two times more accurate than other methods. Our method does not assume the existence of homogeneous areas in the input image and, hence, can successfully process images containing only textures.

317 citations


Proceedings ArticleDOI
01 Dec 2013
TL;DR: It is discovered that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement), revealing that the previously overlooked refinement can be at least as crucial as aggregation.
Abstract: Despite the continuous advances in local stereo matching for years, most efforts are on developing robust cost computation and aggregation methods. Little attention has been seriously paid to the disparity refinement. In this work, we study weighted median filtering for disparity refinement. We discover that with this refinement, even the simple box filter aggregation achieves comparable accuracy with various sophisticated aggregation methods (with the same refinement). This is due to the nice weighted median filtering properties of removing outlier error while respecting edges/structures. This reveals that the previously overlooked refinement can be at least as crucial as aggregation. We also develop the first constant time algorithm for the previously time-consuming weighted median filter. This makes the simple combination ``box aggregation + weighted median'' an attractive solution in practice for both speed and accuracy. As a byproduct, the fast weighted median filtering unleashes its potential in other applications that were hampered by high complexities. We show its superiority in various applications such as depth up sampling, clip-art JPEG artifact removal, and image stylization.

295 citations


Journal ArticleDOI
TL;DR: An adaptive filtering approach based on discrete wavelet transform and artificial neural network is proposed for ECG signal noise reduction that can successfully remove a wide range of noise with significant improvement on SNR (signal-to-noise ratio).

219 citations


Journal ArticleDOI
TL;DR: A new, robust median filtering forensic technique that operates by analyzing the statistical properties of the median filter residual (MFR), which is defined as the difference between an image in question and a median filtered version of itself.
Abstract: In order to verify the authenticity of digital images, researchers have begun developing digital forensic techniques to identify image editing. One editing operation that has recently received increased attention is median filtering. While several median filtering detection techniques have recently been developed, their performance is degraded by JPEG compression. These techniques suffer similar degradations in performance when a small window of the image is analyzed, as is done in localized filtering or cut-and-paste detection, rather than the image as a whole. In this paper, we propose a new, robust median filtering forensic technique. It operates by analyzing the statistical properties of the median filter residual (MFR), which we define as the difference between an image in question and a median filtered version of itself. To capture the statistical properties of the MFR, we fit it to an autoregressive (AR) model. We then use the AR coefficients as features for median filter detection. We test the effectiveness of our proposed median filter detection techniques through a series of experiments. These results show that our proposed forensic technique can achieve important performance gains over existing methods, particularly at low false-positive rates, with a very small dimension of features.

204 citations


Journal ArticleDOI
TL;DR: A new effective noise level estimation method is proposed on the basis of the study of singular values of noise-corrupted images, which can reliably infer noise levels and show robust behavior over a wide range of visual content and noise conditions.
Abstract: Accurate estimation of Gaussian noise level is of fundamental interest in a wide variety of vision and image processing applications as it is critical to the processing techniques that follow. In this paper, a new effective noise level estimation method is proposed on the basis of the study of singular values of noise-corrupted images. Two novel aspects of this paper address the major challenges in noise estimation: 1) the use of the tail of singular values for noise estimation to alleviate the influence of the signal on the data basis for the noise estimation process and 2) the addition of known noise to estimate the content-dependent parameter, so that the proposed scheme is adaptive to visual signals, thereby enabling a wider application scope of the proposed scheme. The analysis and experiment results demonstrate that the proposed algorithm can reliably infer noise levels and show robust behavior over a wide range of visual content and noise conditions, and that is outperforms relevant existing methods.

143 citations


01 Jan 2013
TL;DR: The results of applying different noise types to an image model are presented and a comparative analysis of noise removal techniques is done and the results of various noise reduction techniques are investigated.
Abstract: Getting an efficient method of removing noise from the images, before processing them for further analysis is a great challenge for the researchers. Noise can degrade the image at the time of capturing or transmission of the image. Before applying image processing tools to an image, noise removal from the images is done at highest priority. Ample algorithms are available, but they have their own assumptions, merits and demerits. The kind of the noise removal algorithms to remove the noise depends on the type of noise present in the image. Best results are obtained if testing image model follows the assumptions and fail otherwise. In this paper, light is thrown on some important type of noise and a comparative analysis of noise removal techniques is done. This paper presents the results of applying different noise types to an image model and investigates the results of applying various noise reduction techniques.

142 citations


Journal ArticleDOI
TL;DR: A novel method for the blind detection of MF in digital images is presented and two new feature sets that allow us to distinguish a median- Filtered image from an untouched image or average-filtered one are introduced.
Abstract: Recently, the median filtering (MF) detector as a forensic tool for the recovery of images' processing history has attracted wide interest This paper presents a novel method for the blind detection of MF in digital images Following some strongly indicative analyses in the difference domain of images, we introduce two new feature sets that allow us to distinguish a median-filtered image from an untouched image or average-filtered one The effectiveness of the proposed features is verified with evidence from exhaustive experiments on a large composite image database Compared with prior arts, the proposed method achieves significant performance improvement in the case of low resolution and strong JPEG post-compression In addition, it is demonstrated that our method is more robust against additive noise than other existing MF detectors With analyses and extensive experimental researches presented in this paper, we hope that the proposed method will add a new tool to the arsenal of forensic analysts

126 citations


Journal ArticleDOI
TL;DR: Performance of proposed method is superior to wavelet thresholding, bilateral filter and non-local means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index.
Abstract: Non-local means filter uses all the possible self-predictions and self-similarities the image can provide to determine the pixel weights for filtering the noisy image, with the assumption that the image contains an extensive amount of self-similarity. As the pixels are highly correlated and the noise is typically independently and identically distributed, averaging of these pixels results in noise suppression thereby yielding a pixel that is similar to its original value. The non-local means filter removes the noise and cleans the edges without losing too many fine structure and details. But as the noise increases, the performance of non-local means filter deteriorates and the denoised image suffers from blurring and loss of image details. This is because the similar local patches used to find the pixel weights contains noisy pixels. In this paper, the blend of non-local means filter and its method noise thresholding using wavelets is proposed for better image denoising. The performance of the proposed method is compared with wavelet thresholding, bilateral filter, non-local means filter and multi-resolution bilateral filter. It is found that performance of proposed method is superior to wavelet thresholding, bilateral filter and non-local means filter and superior/akin to multi-resolution bilateral filter in terms of method noise, visual quality, PSNR and Image Quality Index.

125 citations


Journal ArticleDOI
TL;DR: The proposed median filter restores corrupted images with 1-99% levels of salt-and-pepper impulse noise to satisfactory ones by intuitively and simply recognizing impulse noises, while keeping the others intact as nonnoises.

96 citations


Journal ArticleDOI
TL;DR: A new upsampling method that synergistically combines the median and bilateral filters thus it better preserves the depth edges and is more robust to noise.
Abstract: We present a new upsampling method to enhance the spatial resolution of depth images. Given a low-resolution depth image from an active depth sensor and a potentially high-resolution color image from a passive RGB camera, we formulate it as an adaptive cost aggregation problem and solve it using the bilateral filter. The formulation synergistically combines the median and bilateral filters thus it better preserves the depth edges and is more robust to noise. Numerical and visual evaluations on a total of 37 Middlebury data sets demonstrate the effectiveness of our method. A real-time high-resolution depth capturing system is also developed using commercial active depth sensor based on the proposed upsampling method.

Journal ArticleDOI
TL;DR: Experimental evaluation shows the effectiveness of the proposed modifications to the filtering step of the BDND algorithm in producing sharper images than theBDND algorithm.
Abstract: Switching median filters are known to outperform standard median filters in the removal of impulse noise due to their capability of filtering candidate noisy pixels and leaving other pixels intact. The boundary discriminative noise detection (BDND) is one powerful example in this class of filters. However, there are some issues related to the filtering step in the BDND algorithm that may degrade its performance. In this paper, we propose two modifications to the filtering step of the BDND algorithm to address these issues. Experimental evaluation shows the effectiveness of the proposed modifications in producing sharper images than the BDND algorithm.

Journal ArticleDOI
TL;DR: Various filters used to improve image quality, remove the noise, preserves the edges within an image, enhance and smoothen the image are performed namely, average filter, adaptive median filter, average or mean filter, and wiener filter.
Abstract: Presently breast cancer detection is a very important role for worldwide women to save the life. Doctors and radio logistic can miss the abnormality due to inexperience in the field of cancer detection. The pre- processing is the most important step in the mammogram analysis due to poor captured mammogram image quality. Pre-processing is very important to correct and adjust the mammogram image for further study and processing. There are Different types of filtering techniques are available for pre- processing. This filters used to improve image quality, remove the noise, preserves the edges within an image, enhance and smoothen the image. In this paper, we have performed various filters namely, average filter, adaptive median filter, average or mean filter, and wiener filter.

Journal Article
TL;DR: In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques.
Abstract: In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques Digital image is inclined to a variety of noise which affects the quality of image. The main purpose of de-noising the image is to restore the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is corrupting .In the field of reducing the image noise several type of linear and non linear filtering techniques have been proposed . Different approaches for reduction of noise and image enhancement have been considered, each of which has their own limitation and advantages.

Journal ArticleDOI
TL;DR: In this paper, an extension of the scalar median filter to a vector median filter (VMF) was proposed for suppressing noise contained in geophysical data represented by multidimensional, multicomponent vector fields.
Abstract: The scalar median filter (SMF) is often used to reduce noise in scalar geophysical data. We present an extension of the SMF to a vector median filter (VMF) for suppressing noise contained in geophysical data represented by multidimensional, multicomponent vector fields. Although the SMF can be applied to each component of a vector field individually, the VMF is applied to all components simultaneously. Like the SMF, the VMF intends to suppress random noise while preserving discontinuities in the vector fields. Preserving such discontinuities is essential for exploration geophysics because discontinuities often manifest important geologic features such as faults and stratigraphic channels. The VMF is applied to synthetic and field data sets. The results are compared to those generated by using SMF, f-x deconvolution, and mean filters. Our results indicate that the VMF can reduce noise while preserving discontinuities more effectively than the alternatives. In addition, a fast VMF algorithm is descr...

Journal ArticleDOI
TL;DR: A fast non-Bayesian denoising method is proposed that avoids this trade-off by means of a numerical synthesis of a moving diffuser and shows a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement.
Abstract: Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.

Journal ArticleDOI
TL;DR: It is shown that constructing elemental maps of PCA noise filtered data using the background subtraction method, does not guarantee an increase in the signal to noise ratio due to correlation of the spectral data as a result of the filtering process.

Proceedings ArticleDOI
03 Jul 2013
TL;DR: A comparative study of seven filters, namely Lee, Frost, Median, Speckle Reduction Anisotropic Diffusion (SRAD), Perona-Malik's An isotropic diffusion (PMAD) filter, Spekle Reduction Bilateral Filter (SRBF) and Speckel Reduction filter based on soft thresholding in the Wavelet transform, to determine which despeckling algorithm is most effective and optimal for real-time implementation.
Abstract: At present, ultrasound is one of the essential tools for noninvasive medical diagnosis. However, speckle noise is inherent in medical ultrasound images and it is the cause for decreased resolution and contrast-to-noise ratio. Low image quality is an obstacle for effective feature extraction, recognition, analysis, and edge detection; it also affects image interpretation by doctor and the accuracy of computer-assisted diagnostic techniques. Thus, speckle reduction is significant and critical step in pre-processing of ultrasound images. Many speckle reduction techniques have been studied by researchers, but to date there is no comprehensive method that takes all the constraints into consideration. In this paper we discuss seven filters, namely Lee, Frost, Median, Speckle Reduction Anisotropic Diffusion (SRAD), Perona-Malik's Anisotropic Diffusion (PMAD) filter, Speckle Reduction Bilateral Filter (SRBF) and Speckle Reduction filter based on soft thresholding in the Wavelet transform. A comparative study of these filters has been made in terms of preserving the features and edges as well as effectiveness of de-noising.We computed five established evaluation metrics in order to determine which despeckling algorithm is most effective and optimal for real-time implementation. In addition, the experimental results have been demonstrated by filtered images and statistical data table.

Journal ArticleDOI
TL;DR: The results of this study showed that the 2D FIR filters designed based on ABC optimization can eliminate speckle noise quite well on noise added test images and intrinsically noisy ultrasound images.

01 Jan 2013
TL;DR: This Paper reviews on various noises like Salt and Pepper noise, Gaussian noise etc and various techniques available for denoising the color image.
Abstract: Images are often degraded by noises. Noise can occur during image capture, transmission, etc. Noise removal is an important task in Image processing. In general the results of the noise removal have a strong influence on the quality of the image processing technique. Several techniques for noise removal are well established in color image processing. The nature of the noise removal problem depends on the type of the noise corrupting the im age. In the field of image noise reduction several linear and non linear filtering methods have been proposed. Denoising of image is very important and inverse problem of image processing which is useful in the areas of image mining, image segmentation, pattern recognition and an important preprocessing technique to remove the noise from the naturally corrupted image by the different types of noises. The wavelet techniques are very effective to remove the noise also use of its capability to confine the power of a signal in little convert of energy values. This Paper reviews on various noises like Salt and Pepper noise, Gaussian noise etc and various techniques available for denoising the color image.

Journal ArticleDOI
TL;DR: The results suggest that DL-INR has a better ability to suppress impulse noise than other six algorithms and can produce restored images with higher peak signal-to-noise ratio (PSNR).

Journal ArticleDOI
TL;DR: The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent, and it is demonstrated that Fisher information on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range).
Abstract: We deal with the problem of blind parameter estimation of signal-dependent noise from mono-component image data. Multispectral or color images can be processed in a component-wise manner. The main results obtained rest on the assumption that the image texture and noise parameters estimation problems are interdependent. A two-dimensional fractal Brownian motion (fBm) model is used for locally describing image texture. A polynomial model is assumed for the purpose of describing the signal-dependent noise variance dependence on image intensity. Using the maximum likelihood approach, estimates of both fBm-model and noise parameters are obtained. It is demonstrated that Fisher information (FI) on noise parameters contained in an image is distributed nonuniformly over intensity coordinates (an image intensity range). It is also shown how to find the most informative intensities and the corresponding image areas for a given noisy image. The proposed estimator benefits from these detected areas to improve the estimation accuracy of signal-dependent noise parameters. Finally, the potential estimation accuracy (Cramer-Rao Lower Bound, or CRLB) of noise parameters is derived, providing confidence intervals of these estimates for a given image. In the experiment, the proposed and existing state-of-the-art noise variance estimators are compared for a large image database using CRLB-based statistical efficiency criteria.

Journal ArticleDOI
TL;DR: In this paper, the authors mainly studied the disease of cucumber downy mildew, powdery mew and anthracnose leaf image processing and recognition technologies, they mainly applied median filtering method of filtering noise, leaf spot disease, and extract color feature parameters of lesion site, characteristic parameters of the shape; extraction texture parameters by using gray level co-occurrence matrix.
Abstract: This paper mainly studies the disease of cucumber downy mildew, powdery mildew and anthracnose leaf image processing and recognition technologies. Application of median filtering method of filtering noise, leaf spot disease of cucumber leaf color range segmentation part extract color feature parameters of the lesion site, characteristic parameters of the shape; extraction texture parameters by using gray level co-occurrence matrix. Based on the shortest distance methods to identify diseases of images. The experimental result showed that the current method on disease recognition accuracy rates more than 96%.

Journal ArticleDOI
TL;DR: A simple explicit image filter which can filter out noise while preserving edges and fine-scale details is derived, which has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks.
Abstract: In this paper, we propose a novel approach for performing high-quality edge-preserving image filtering. Based on a local linear model and using the principle of Stein's unbiased risk estimate as an estimator for the mean squared error from the noisy image only, we derive a simple explicit image filter which can filter out noise while preserving edges and fine-scale details. Moreover, this filter has a fast and exact linear-time algorithm whose computational complexity is independent of the filtering kernel size; thus, it can be applied to real time image processing tasks. The experimental results demonstrate the effectiveness of the new filter for various computer vision applications, including noise reduction, detail smoothing and enhancement, high dynamic range compression, and flash/no-flash denoising.

Journal ArticleDOI
TL;DR: This paper identifies a new digital watermarking approach for copyright protection of video based on wavelet transformation with good performance for transparency and robustness and the robustness of the proposed method against various kinds of attacks including MPEG-4, MPEG-2, and H.264.
Abstract: Due to the replicable nature of video many illegal copies of the original video can be made. So it demands to deliver methods for preventing illegal copying. This paper identifies a new digital watermarking approach for copyright protection of video based on wavelet transformation. First, the motion part of color video is detected by scene change analysis, and then by applying 3D wavelet transformation over detected motion part, 10 sub-bands of wavelet coefficients are obtained. In order to insert the watermark, 3D coefficients of HL, LH and HH with their third level are selected. After all, by using a spread spectrum technique, the watermark is embedded into the selected wavelet coefficients. In extraction step, the original video is not needed, namely, blind detection. So, the resultant watermarking scheme can be used for public watermarking applications, where the original video is not available for watermark extraction. The experimental results show a good performance of the proposed method for transparency and robustness. Furthermore, the robustness of the proposed method against various kinds of attacks such as median filtering, Gaussian noise, frame dropping, frame averaging, frame swapping and lots of lossy compression including MPEG-4, MPEG-2, and H.264 shows the fidelity of our claim.

Journal ArticleDOI
TL;DR: This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images and demonstrates state-of-the-art performance over a wide range of noise levels, with low computational complexity.
Abstract: This paper adapts the least-squares luma-chroma demultiplexing (LSLCD) demosaicking method to noisy Bayer color filter array (CFA) images. A model is presented for the noise in white-balanced gamma-corrected CFA images. A method to estimate the noise level in each of the red, green, and blue color channels is then developed. Based on the estimated noise parameters, one of a finite set of configurations adapted to a particular level of noise is selected to demosaic the noisy data. The noise-adaptive demosaicking scheme is called LSLCD with noise estimation (LSLCD-NE). Experimental results demonstrate state-of-the-art performance over a wide range of noise levels, with low computational complexity. Many results with several algorithms, noise levels, and images are presented on our companion web site along with software to allow reproduction of our results.

Journal ArticleDOI
TL;DR: A fuzzy rule system is used to assign the weights in the averaging so that both noise types are reduced and image structures are preserved and the performance of the method is competitive with respect to state-of-the-art filters.
Abstract: Mixed impulsive and Gaussian noise reduction from digital color images is a challenging task because it is necessary to appropriately process both types of noise that in turn need to be distinguished from the original image structures such as edges and details. Fuzzy theory is useful to build simple, efficient, and effective solutions for this problem. In this paper, we propose a fuzzy method to reduce Gaussian and impulsive noise from color images. Our method uses one only filtering operation: a weighted averaging. A fuzzy rule system is used to assign the weights in the averaging so that both noise types are reduced and image structures are preserved. We provide experimental results to show that the performance of the method is competitive with respect to state-of-the-art filters.

Journal ArticleDOI
TL;DR: The results show that the proposed method offers improved performance compared with several methods from the literature, especially under additive noise and compression attacks.
Abstract: A new method for video watermarking is presented in this paper. In the proposed method, data are embedded in the LL subband of wavelet coefficients, and decoding is performed based on the comparison among the elements of the first principal component resulting from empirical principal component analysis (PCA). The locations for data embedding are selected such that they offer the most robust PCA-based decoding. Data are inserted in the LL subband in an adaptive manner based on the energy of high frequency subbands and visual saliency. Extensive testing was performed under various types of attacks, such as spatial attacks (uniform and Gaussian noise and median filtering), compression attacks (MPEG-2, H. 263, and H. 264), and temporal attacks (frame repetition, frame averaging, frame swapping, and frame rate conversion). The results show that the proposed method offers improved performance compared with several methods from the literature, especially under additive noise and compression attacks.

Journal ArticleDOI
TL;DR: A prewhitening procedure is proposed to whiten noise in HSIs, and a multidimensional wavelet packet transform (MWPT) in tensor form is presented to find different component tensors of the HSI.
Abstract: Denoising is an important preprocessing step for several applications in the hyperspectral imaging (HSI) domain, such as classification and target detection, to achieve good performances Because the signal-dependent photonic noise has become as dominant as the signal-independent noise generated by the electronic circuitry in HSI data collected by new-generation hyperspectral sensors, the reduction of the additive signal-dependent photonic noise becomes the focus of the current research in this field To reduce the optoelectronic noise from HSIs, a new method is developed in this paper First, a prewhitening procedure is proposed to whiten noise in HSIs Second, a multidimensional wavelet packet transform (MWPT) in tensor form is presented to find different component tensors of the HSI Then, to jointly filter a component tensor in each mode, a multiway Wiener filter is introduced Moreover, to determine the best transform level and basis of the MWPT, a risk function is proposed The effectiveness of our method in denoising and classification is experimentally demonstrated on a real-world HSI acquired by an airborne sensor

Journal ArticleDOI
TL;DR: It is shown, through a number of experiments with synthetic, phantom, and in vivo data, that neglecting the correlated nature of noise in multiple-coil systems implies important errors even in the simplest cases and the proper statistical characterization of noise through effective parameters drives to improved accuracy for both of the problems studied.