Showing papers on "Dark-frame subtraction published in 2016"

An Efficient Adaptive Fuzzy Switching Weighted Mean Filter for Salt-and-Pepper Noise Removal

Abstract: An image degraded by noise is a common phenomenon. In this letter, we propose a novel adaptive fuzzy switching weighted mean filter to remove salt-and-pepper (SAP) noise. The process of denoising includes two stages: noise detection and noise elimination. In the first stage, pixels in a corrupted image are classified into two categories: original pixels and possible noise pixels. For the latter, we compute the maximum absolute luminance difference of processed pixels next to possible noise pixels to classify them into three categories: uncorrupted pixels, lightly corrupted pixels, and heavily corrupted pixels. In the second stage, under the assumption that pixels at a short distance tend to have similar values, the distance relevant weighted mean of the original pixels in the neighborhood of a noise pixel are computed. For a nonnoise pixel, retain it as unchanged; for a lightly corrupted pixel, replace it with the weighted average value of the weighted mean and its own value; and for a heavily corrupted pixel, change it to be the weighted mean. Experimental results show that compared to some state-of-the-art algorithms, our method keeps more texture details and is better at removing SAP noise and depressing artifacts.

Effective Strip Noise Removal for Low-Textured Infrared Images Based on 1-D Guided Filtering

Abstract: Infrared images typically contain obvious strip noise. It is a challenging task to eliminate such noise without blurring fine image details in low-textured infrared images. In this paper, we introduce an effective single-image-based algorithm to accurately remove strip-type noise present in infrared images without causing blurring effects. First, a 1-D row guided filter is applied to perform edge-preserving image smoothing in the horizontal direction. The extracted high-frequency image part contains both strip noise and a significant amount of image details. Through a thermal calibration experiment, we discover that a local linear relationship exists between infrared data and strip noise of pixels within a column. Based on the derived strip noise behavioral model, strip noise components are accurately decomposed from the extracted high-frequency signals by applying a 1-D column guided filter. Finally, the estimated noise terms are subtracted from the raw infrared images to remove strips without blurring image details. The performance of the proposed technique is thoroughly investigated and is compared with the state-of-the-art 1-D and 2-D denoising algorithms using captured infrared images.

Simultaneous enhancement and noise reduction of a single low-light image

Abstract: Images obtained under low-light conditions tend to have the characteristics of low-grey levels, high-noise levels, and indistinguishable details. Image degradation not only affects the recognition of images, but also influences the performance of the computer vision system. The low-light image enhancement algorithm based on the dark channel prior de-hazing technique can enhance the contrast of images effectively and can highlight the details of images. However, the dark channel prior de-hazing technique ignores the effects of noise, which leads to significant noise amplification after the enhancement process. In this study, a de-hazing-based simultaneous enhancement and noise reduction algorithm of are proposed by analysing the essence of the dark channel prior de-hazing technique and bilateral filter. First, the authors estimate the values of the initial parameters of the hazy image model by de-hazing technique. Then, they correct the parameters of the hazy image model alternately with the iterative joint bilateral filter. Experimental results indicate that the proposed algorithm can simultaneously enhance the low-light images and reduce noise effectively. The proposed algorithm could also perform quite well compared with the current common image enhancement and noise reduction algorithms in terms of the subjective visual effects and objective quality assessments.

Analysis and effects of speckle noise in SAR images

Abstract: In the field of image restoration, noise plays the most prominent role. Speckle noise is a granular pattern, special kind of noise that mainly found in the satellite images, removing such noise is one of the major challenge and least touched issue. These satellite images are captured by special kind of radar named as Synthetic Aperture Radar. Speckle noise is an undesirable effect. The source of this type of noise is caused due to random interference between the coherent returns issued from the so many scatterers present on a earth surface, on the scale of a wavelength of the incident radar wave. In general, speckle noise is the grainy salt-and-pepper pattern present in radar imagery. This paper analyses the effects of speckle noise into the SAR image using probability density function.

Adaptive noise-reducing anisotropic diffusion filter

Abstract: In image processing and computer vision, the denoising process is an important step before several processing tasks. This paper presents a new adaptive noise-reducing anisotropic diffusion (ANRAD) method to improve the image quality, which can be considered as a modified version of a speckle-reducing anisotropic diffusion (SRAD) filter. The SRAD works very well for monochrome images with speckle noise. However, in the case of images corrupted with other types of noise, it cannot provide optimal image quality due to the inaccurate noise model. The ANRAD method introduces an automatic RGB noise model estimator in a partial differential equation system similar to the SRAD diffusion, which estimates at each iteration an upper bound of the real noise level function by fitting a lower envelope to the standard deviations of pre-segment image variances. Compared to the conventional SRAD filter, the proposed filter has the advantage of being adapted to the color noise produced by today's CCD digital camera. The simulation results show that the ANRAD filter can reduce the noise while preserving image edges and fine details very well. Also, it is favorably compared to the fast non-local means filter, showing an improvement in the quality of the restored image. A quantitative comparison measure is given by the parameters like the mean structural similarity index and the peak signal-to-noise ratio.

Determining Conversion Gain and Read Noise Using a Photon-Counting Histogram Method for Deep Sub-Electron Read Noise Image Sensors

Abstract: A new method for characterizing deep sub-electron read noise image sensors is reported. This method, based on the photon-counting histogram, can provide easy, independent and simultaneous measurements of the quanta exposure, conversion gain, and read noise. This new method provides a more accurate measure of conversion gain and read noise over conventional characterization techniques for image sensors with read noise from 0.15–0.40e- rms.

Noise Power Spectrum Measurements in Digital Imaging With Gain Nonuniformity Correction

Abstract: The noise power spectrum (NPS) of an image sensor provides the spectral noise properties needed to evaluate sensor performance. Hence, measuring an accurate NPS is important. However, the fixed pattern noise from the sensor’s nonuniform gain inflates the NPS, which is measured from images acquired by the sensor. Detrending the low-frequency fixed pattern is traditionally used to accurately measure NPS. However, detrending methods cannot remove high-frequency fixed patterns. In order to efficiently correct the fixed pattern noise, a gain-correction technique based on the gain map can be used. The gain map is generated using the average of uniformly illuminated images without any objects. Increasing the number of images $n$ for averaging can reduce the remaining photon noise in the gain map and yield accurate NPS values. However, for practical finite $n$ , the photon noise also significantly inflates NPS. In this paper, a nonuniform-gain image formation model is proposed and the performance of the gain correction is theoretically analyzed in terms of the signal-to-noise ratio (SNR). It is shown that the SNR is $ {\textit{O}}\left ({\sqrt {n}}\right )$ . An NPS measurement algorithm based on the gain map is then proposed for any given $n$ . Under a weak nonuniform gain assumption, another measurement algorithm based on the image difference is also proposed. For real radiography image detectors, the proposed algorithms are compared with traditional detrending and subtraction methods, and it is shown that as few as two images ( $n=1$ ) can provide an accurate NPS because of the compensation constant $(1+1/n)$ .

On the efficiency of techniques for the reduction of impulsive noise in astronomical images

Abstract: The impulsive noise in astronomical images originates from various sources. It develops as a result of thermal generation in pixels, collision of cosmic rays with image sensor or may be induced by high readout voltage in Electron Multiplying CCD (EMCCD). It is usually efficiently removed by employing the dark frames or by averaging several exposures. Unfortunately, there are some circumstances, when either the observed objects or positions of impulsive pixels evolve and therefore each obtained image has to be filtered independently. In this article we present an overview of impulsive noise filtering methods and compare their efficiency for the purpose of astronomical image enhancement. The employed set of noise templates consists of dark frames obtained from CCD and EMCCD cameras working on ground and in space. The experiments conducted on synthetic and real images, allowed for drawing numerous conclusions about the usefulness of several filtering methods for various: (1) widths of stellar profiles, (2) signal to noise ratios, (3) noise distributions and (4) applied imaging techniques. The results of presented evaluation are especially valuable for selection of the most efficient filtering schema in astronomical image processing pipelines.

PARIGI: a Patch-based Approach to Remove Impulse-Gaussian Noise from Images

Abstract: In this paper, we present an implementation of the PARIGI method that addresses the problem of the restoration of images affected by impulse noise or by a mixture of Gaussian and impulse noise. The method relies on a patch-based approach, which requires careful choices for both the distance between patches and for the statistical estimator of the original patch. Experiments are performed in the case of pure impulse noise and in the case of a mixture of Gaussian and impulse noise.

Quality assessment of Median filtering techniques for impulse noise removal from digital images

Abstract: Impulse noise still poses challenges in front of researchers today. The removal of impulse noise brings blurring which leads to edges being distorted and image thus being of poor quality. Hence the need is to preserve edges and fine details during filtering. The proposed method consists of noise detection and then removal of detected noise by Improved Adaptive Median Filter using pixels that are not noise themselves in gray level as well as colour images. The pixels are split in two groups, which are noise-free pixels and noisy pixels. In removing out Impulse noise, only noisy pixels are processed. The noiseless pixels are then sent directly to the output image. The proposed method adaptively changes the masking matrix size of the median filter based on the count of the noisy pixels. Computer simulation and analysis have been carried out eventually to analyse the performance of the proposed method with that of Simple Median Filter (SMF), Simple Adaptive Median Filter (SAMF) and Adaptive Switched Median Filter (ASMF). The proposed filter proves to be more efficient in terms of both objective and subjective parameters.

Clustered impulse noise removal from color images with spatially connected rank filtering

Abstract: This paper deals with impulse noise removal from color images. The proposed noise removal algorithm employs two classical approaches for color image denoising; that is, detection of corrupted pixels and removal of the detected noise by means of local rank filtering. With the help of computer simulation we show that the proposed algorithm can effectively remove impulse noise and clustered impulse noise. The performance of the proposed algorithm is compared in terms of image restoration metrics with that of common successful algorithms.

A kind of effective method of removing compound noise in image

Abstract: The noise is generally not a single type in digital image, which is usually composed of Gaussian noise and pulse noise. So the signal to noise ratio of the image is low and the noise in images needs to be processed. The traditional denoising algorithm and various improved algorithm is effective for single Gaussian noise or impulse noise mostly, these denoising algorithms are unable to obtain satisfactory denoising effect in dealing with the mixed noise. Some existing hybrid denoising methods usually change distribution characteristics of another noise when removing one noise at the same time, so subsequent denoising effect is affected. To resolve the problem, a composite image denoising method based on the combination of independent component analysis and traditional spatial denoising method is proposed. Simulation experiment results show that the method can effectively preserve edge details and other information of image when removing mixed noise.

Noise Resistant Fusion for Multi-Exposure Sensors

Abstract: A noise resistant image fusion scheme for multi-exposure sensors using color dissimilarity (for motion detection and removal), median and noise maps (to refine weights for noise removal) is proposed. A well-exposed image is obtained as a result of weighted average of multi-exposure source images. Higher valued weights are assigned to pixels containing low values of noises, high values of color dissimilarity, and median maps. Quantitative and qualitative comparison reveals superiority of the proposed scheme as compared with the current state-of-the-art multi-exposure fusion schemes.

Real-time removal of random value impulse noise in medical images

Abstract: With the increasing use of telemedicine there is a great demand in real-time processing and transmission of medical images. Noise is one of the important factors that degrade the quality of medical images. Impulse noise is a common noise that could be caused by malfunctioning of sensors or by data transmission errors. It is one the most common noises that have extensively been studied in recent years. For real-time noise removal hardware techniques are more suited, since software methods are complex and slow. Usually hardware techniques have low complexity and low accuracy. In this paper a low complexity, high accuracy, de-noising method is proposed. It first categorizes image pixels into a number of groups. Then noisy pixels are restored in different ways in each category. Local analysis of image blocks allows us to restore a noisy pixel by using its neighboring non-noisy pixels. All steps are designed to have low hardware complexity. Simulation results show that in the case of MR images, the proposed method removes impulse noise with acceptable accuracy.

Automatic Image Splicing Detection Based on Noise Density Analysis in Raw Images

Abstract: Image splicing is a common manipulation which consists in copying part of an image in a second image. In this paper, we exploit the variation in noise characteristics in spliced images, caused by the difference in camera and lighting conditions during the image acquisition. The proposed method automatically gives a probability of alteration for any area of the image, using a local analysis of noise density. We consider both Gaussian and Poisson noise components to modelize the noise in the image. The efficiency and robustness of our method is demonstrated on a large set of images generated with an automated splicing.

An effective self-adaptive mean filter for mixed noise

Abstract: The image is usually corrupted by Gauss noise and impulse noise simultaneously, and its quality will be reduced Thus filtering image is important in image processing The traditional mean filter cannot remove impulse noise effectively while preserve the details of image well And the median filter cannot remove Gauss noise effectively In this paper, we propose the self-adaptive mean filter to remove mixed noise of Gauss noise and impulse noise Firstly, dividing pixels of image into good pixels and corrupted pixels based on whether there are noises in their small neighborhoods And the greyscale value of good pixels are output directly Secondly, for corrupted pixels, removing Gauss noises and impulse noises respectively based on characteristics of different noise The results demonstrate that the self-adaptive mean filter can eliminate mixed noise of different density better and preserve the details of image better comparing with the mean filter or the median filter for mixed noise

No-reference image noise estimation based on noise level accumulation

Abstract: In this paper, a method of no-reference image noise assessment is presented, which utilizes the estimated noise level accumulation (NLA) index value. The affine reconstruction model is applied after segmenting the noisy image into several patches. Boundary blur process is conducted to smooth the segmentation edges. For each image patch the mean value standing for brightness and the standard deviation value indicating the noise standard deviation are computed to give the noise samples estimation. The accurate image noise standard deviation is estimated by integrating NLA index value of several overlapped intervals combined with different visual weights. Experiment results are provided to demonstrate that the proposed method performs well for images with different contents over a large range of noise levels both monotonously and accurately. Comparisons against other conventional approaches are also carried out to exhibit the superior performance of the proposed algorithm.

A local adaptive threshold noise detection linear interpolation filter (LALIF) for stripe noise removal in infrared images

Abstract: Stripe noise is usually introduced into infrared images during acquisition, which seriously deteriorates the image quality and affects the subsequent analysis of the image. In this paper, the spatial mathematic model and the comb-like impulse spectrum characteristics in Fourier domain of stripe noise are given. On this basis, a local adaptive threshold noise detection linear interpolation filter (LALIF) for stripe noise removal in infrared images is designed. In the filter method, the first, we crop the original image into two sub-images with optimal size to make the comb-like impulse spectrum characteristics of the stripe noise most prominent. The second, we use a local adaptive threshold to distinguish noise frequency points from the useful frequency points in the peak regions of the amplitude spectrum, and reassign the value of noise frequency points by linear interpolation. At last, we amalgamate the sub-images to obtain the stripe noise removal image. Experimental results indicate that the proposed method has a good performance in adaptability and stability for complex stripe noise removal in infrared images.

Defect signal to noise enhancement by reducing die to die process noise

Abstract: Gray level histograms for a test image and a reference image are adjusted by histogram scaling. Parameters from the histogram scaling are applied to the test image and the reference image. After the parameters are applied, the reference image and the test image are compared to produce a difference image, such as by subtracting the reference image from the test image. Noise in the difference image can be reduced, which improves defect identification in the difference image. In addition, noisy structures in the difference image which are elongated in vertical or horizontal direction can be found. If the noise exceeds a certain threshold, the structures may not be inspected.

Design and implementation of adaptive Gaussian filters for the removal of salt and pepper noise on FPGA

Abstract: In recent eras of image processing, during image acquisition and transfer, images are often corrupted by impulse noise which is a major factor affecting the contents of a digital image. In fixed-value impulse noise, the gray value is a fixed value, i.e., either 0 or 255 (example: Salt and pepper noise). Digital signal processing frequently involves some method for noise reduction over an image. The median filter is a non-linear digital filter, which is most popularly used to remove impulse noises. Many popular algorithms were presented which eliminates impulse noises present and maintains the fine details of the image. But, median filter fails to preserves the edges of the image by uniform modification of the noise affected pixels and the noise-free pixels. Also, the conventional filters work better only over images affected with low noise ratios and is very poor when the noise ratio reaches above 40%. The proposed de-noising algorithm uses an adaptive Gaussian filters for removal of salt and pepper noise. As conventional de-noising algorithm fails at high noise density and in preserving edges along with image details the proposed technique switches between mask sizes depending noise density. To preserve edges and details of the image Adaptive threshold is employed. Advantage of adaptive threshold filtering does not affect the edges or other small structures in the image. Hence this method is more efficient for the images with very high noise ratio and preserving edges.

Mastering the Masters

Abstract: Not all pixels on a CCD chip are created equal. To make accurate photometric measurements, the differences in sensitivity and average noise must be taken into account. This is done through the use of master flat fields, dark frames, and bias frames. This chapter describes each type of master and how to create them.

Reduce noise in the binary image using non linear spatial filtering of mode

Abstract: Noise is a problem that is often encountered when separating the object from the background in the binary image. Noise may occur in the background and the object, can be spot or patchy and the tassel is connected to the object. The research segmentation of pupil object from eye image, tries to using non-linear spatial filtering of mode to reduce noise in the binary image. Using this filtering at the binary image, can be reduced the both of black noise at the background and the white noise at the object image. Also reduce spot noise and tassel noise.

Image processing device and noise reduction system having the same

Abstract: An image processing device includes a bad pixel masking unit and a noise reduction unit. The bad pixel masking unit detects bad pixels in a plurality of blocks and masks the determined bad pixels. The noise reduction unit removes noise from the pixels in the plurality of blocks excluding the masked pixels using a pattern matching method and corrects bad pixels by obtained an average pixel value.

A novel approach to remove random-valued impulse noise from digital image

Abstract: In the field of switching filter, a highly effective filter to restore extremely corrupted image with impulse noise is presented. It is capable of handling low density as well as high density of random valued and fixed valued impulse noise. In this study, local area comprises within the window in an image is analyzed for intensity extrema to classify the pixel as either noisy or noiseless. Filtering is applied to the noisy pixels only and it is done in such a way that the noisy pixel is replaced by either the median or the mean value of the filtering window depending on the noiseless pixels present in the window. The window size is adaptive for this filter and depends on the estimated noise density. The proposed filter is tested on a large number of grayscale and color images under a wide range of noise density (from 10% to 94%) and the simulation results reveal that it performs better than other approaches to impulse noise removal, in terms of suppressing impulse noise while preserving image details. The proposed filter is simple to implement and suitable for real time implementation.

An efficient restoration algorithm for images corrupted with salt and pepper noise

Abstract: This paper presents a two-step restoration algorithm for impulse noise detection and removal. In the detection step, the pixel which is most likely corrupted by noise is detected according to its gray values. In the removal step, the proposed algorithm adaptively alters the filtering window size depending on the noise density. For a noisy pixel, if there exist one or more noise-free pixels in its window, the spatial correlation-based weighted mean filter will be applied to it by using only noise-free pixels. Otherwise, we use the median filter to correct the detection errors and remove noise. Naturally, the noise-free pixels are retained. Experimental results show that compared with the other filters, our algorithm can provide better performances in both quantitatively and visually.

Local denoising based on curvature smoothing can visually outperform non-local methods on photographs with actual noise

Abstract: We propose a fast, local denoising method where the Euclidean curvature of the noisy image is approximated in a regularizing manner and a clean image is reconstructed from this smoothed curvature. User preference tests show that when denoising real photographs with actual noise our method produces results with the same visual quality as the more sophisticated, nonlocal algorithms Non-local Means and BM3D, but at a fraction of their computational cost. These tests also highlight the limitations of objective image quality metrics like PSNR and SSIM, which correlate poorly with user preference.

Reduction of mosquito noise based on the adaptive bilateral-filter

Abstract: Compressing digital image reduces the total size of the digital image file. However, sometimes the processing and displaying of the compressed digital image may lead to “mosquito Noise”. Mosquito Noise derogates the visual effects of the compressed digital image, so in this paper, the reduction of mosquito noise based on the adaptive bilateral-filter is proposed. The gray variation of a pixel's neighborhood region can be used to judge whether the pixel is in the vicinity of the edge. By using gray variation of pixels in the detection window, bilateral-filter's spatial variances and gray variances are adjusted, so that the filtering strength can be adjusted adaptively. Strong filtering is executed for the pixels in the vicinity of the edge, and weak filtering for the pixels in the details. Flat areas are not filtered. The proposed method makes up the shortcoming that traditional reduction of the mosquito noise leads blur to the edges and details by filtering the whole image blindly. It reduces the mosquito noise maximally with details preserved, so as to avoid blurring the image when it is processed.

Research of properties of digital noise in contrast images

Abstract: The article is devoted to the research of probabilistic properties of digital noise in contrast images. For obtaining numerical characteristics of the additive noise distribution physical experiments were made. These characteristics testify that noise in digital images is non-uniform both on variance and on distribution are received were for this purpose made. The image with contrast overfall from black to white was simulated and the noise filtration is carried out by known methods of smoothing. During a Monte-Carlo method of statistical tests it was established that smoothing based on the generalized method of the least absolute values eliminates noise on contrast overfall more efficiently.