Showing papers on "Dark-frame subtraction published in 2015"

A Review Paper: Noise Models in Digital Image Processing.

Abstract: Noise is always presents in digital images during image acquisition, coding, transmission, and processing steps. Noise is very difficult to remove it from the digital images without the prior knowledge of noise model. That is why, review of noise models are essential in the study of image denoising techniques. In this paper, we express a brief overview of various noise models. These noise models can be selected by analysis of their origin. In this way, we present a complete and quantitative analysis of noise models available in digital images.

Quanta Image Sensor Jot With Sub 0.3e- r.m.s. Read Noise and Photon Counting Capability

Abstract: The first quanta image sensor jot with photon counting capability is demonstrated. The low-voltage device demonstrates less than 0.3e- r.m.s. read noise on a single read out without the use of avalanche gain and single-electron signal quantization is observed. A new method for determining read noise and conversion gain is also introduced.

Adaptive rank weighted switching filter for impulsive noise removal in color images

Abstract: In this paper, a novel approach to the problem of impulsive noise removal in color digital images is presented. The described switching filter is based on the rank weighted, cumulated pixel dissimilarity measures, which are used for the detection of image samples contaminated by impulsive noise process. The introduced adaptive design enables the filter to tune its parameters to the amount of impulsive noise corrupting the image. The comparison with existing denoising schemes shows that the new technique more efficiently removes the impulses introduced by the noise process, while better preserving image details. An important feature of the new filter is its low computational complexity, which allows for its application in real-time applications.

Characterization of Quanta Image Sensor Pump-Gate Jots With Deep Sub-Electron Read Noise

Abstract: Characterization of quanta image sensor pixels with deep sub-electron read noise is reported. Pixels with conversion gain of $423\mu \text{V}$ /e- and read noise as low as 0.22e- r.m.s. were measured. Dark current is 0.1e-/s at room temperature, and lag less than 0.1e-. This is one of the first works reporting detailed characterization of image sensor pixels with mean signals from sub-electron (0.25e-) to a few electrons level. Such pixels in a nearly-conventional CMOS image sensor process will allow realization of photon-counting image sensors for a variety of applications.

Image Noise and Digital Image Forensics

Abstract: Noise is an intrinsic specificity of all forms of imaging, and can be found in various forms in all domains of digital imagery. This paper offers an overall review of digital image noise, from its causes and models to the degradations it suffers along the image acquisition pipeline. We show that by the end of the pipeline, the noise may have widely different characteristics compared to the raw image, and consider the consequences in forensic and counter-forensic imagery.

Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering

Abstract: In this letter, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.

Distance Measurement Error in Time-of-Flight Sensors Due to Shot Noise

Abstract: Unlike other noise sources, which can be reduced or eliminated by different signal processing techniques, shot noise is an ever-present noise component in any imaging system. In this paper, we present an in-depth study of the impact of shot noise on time-of-flight sensors in terms of the error introduced in the distance estimation. The paper addresses the effect of parameters, such as the size of the photosensor, the background and signal power or the integration time, and the resulting design trade-offs. The study is demonstrated with different numerical examples, which show that, in general, the phase shift determination technique with two background measurements approach is the most suitable for pixel arrays of large resolution.

A Morphological Mean Filter for Impulse Noise Removal

Abstract: Median filtering computation for noise removal is often used in impulse noise removal techniques, but the difficulties in removing high-density noise aspect restrict its development. In this paper, we propose a very efficient method to restore image corrupted by high-density impulse noise. First, the proposed method detects both the number and position of the noise-free pixels in the image. Next, the dilatation operation of the noise-free pixels based on morphological image processing is iteratively executed to replace the neighbor noise pixels until convergence. By doing so, the proposed method is capable to remove high-density noise and therefore reconstruct the noise-free image. Experimental results indicate that the proposed method more effectively removes high-density impulse noise in corrupted images in comparison with the other tested state-of-the-art methods. Additionally, the proposed method only requires moderate execution time to achieve optimal impulse noise removal.

Median Filter Performance Based on Different Window Sizes for Salt and Pepper Noise Removal in Gray and RGB Images

Abstract: Noised image is a major problem of digital image systems when images transferred between most electronic communications devices. Noise occurs in digital images due to transmit the image through the internet or maybe due to error generated by noisy sensors. Other types of errors are related to the communication system itself, since image needed to be transferred from analogue to digital and vice versa, also to be transmitted in most of the communication systems. Impulse noise added to image signal due to process of converting image signal or error from communication channel. The noise added to the original image by changes the intensity of some pixels while other remain unchanged. Salt-and-pepper noise is one of the impulse noises, to remove it a simplest way used by windowing the noisy image with a conventional median filter. Median filters are the most popular nonlinear filters extensively applied to eliminate saltand-pepper noise. This paper evaluates the performance of median filter based on the effective median per window by using different window sizes and cascaded median filters. The performance of the proposed idea has been evaluated in MATLAB simulations on a gray and RGB images. The experimental results show that median filter has a good performance in gray and RGB images in low noise densities and also in high noise densities when using cascaded median filter and high level of window sizes, but with higher window size a degree of blurring effect will be added to filtered noise.

Color image noise removal by modified adaptive threshold median filter for RVIN

Abstract: In the field of digital image processing impulse noise removal from color images is one of the most challenging tasks for researchers. A Modified Adaptive Threshold Median Filter (MATMF) is presented for impulse noise removal for both low and high-density noise levels. In this paper, we have proposed filter for color image random valued impulse noise reduction. For color images, impulse noise removal there are two main stages, firstly, the detection of the impulse noises on the basis of maximum and minimum value of pixels in a small window. In the second stage removal of noise on the basics median calculation. In the filtering stage, the noise-free pixels remain unchanged in all RGB frames, windows and noisy pixels are restored using a median filter. Experimental results show that proposed method is superior to the conventional methods in Peak Signal Noise Ratio [8] (PSNR) values and Mean Square Error (MSE) of the image performs, with and without noise compared. The mathematical analysis describes that the analysis of the noisy pixels and use of noise-free pixels for the de-noising purpose provide much better results and provides better visual quality of de-noised image and provide good quality in human persecution.

A denoising of biomedical images

Abstract: . Today imaging science has an important development and has many applications in different fields of life. The researched object of imaging science is digital image that can be created by many digital devices. Biomedical image is one of types of digital images. One of the limits of using digital devices to create digital images is noise. Noise reduces the image quality. It appears in almost types of images, including biomedical images too. The type of noise in this case can be considered as combination of Gaussian and Poisson noises. In this paper we propose method to remove noise by using total variation. Our method is developed with the goal to combine two famous models: ROF for removing Gaussian noise and modified ROF for removing Poisson noise. As a result, our proposed method can be also applied to remove Gaussian or Poisson noise separately. The proposed method can be applied in two cases: with given parameters (generated noise for artificial images) or automatically evaluated parameters (unknown noise for real images).

A Spatial Mean and Median Filter For NoiseRemoval in Digital Images

Abstract: In this project, Mean and Median image filtering algorithms are compared based on their ability to reconstruct noise affected images. The purpose of these algorithms is to remove noise from a signal that might occur through the transmission of an image. In software, a smoothing filter is used to remove noise from an image. Each pixel is represented by three scalar values representing the red, green, and blue chromatic intensities. At each pixel studied, a smoothing filter takes into account the surrounding pixels to derive a more accurate version of this pixel. By taking neighbouring pixels into consideration, extreme “noisy” pixels can be replaced. However, outlier pixels may represent uncorrupted fine details, which may be lost due to the smoothing process. This project examines two common smoothing algorithms. These algorithms can be applied to one-dimensional as well as two-dimensional signals. For each of the two algorithms discussed, experimental results will be shown that indicate which algorithm is best suited for the purpose of impulse noise removal in digital color images.

Automated Image Splicing Detection from Noise Estimation in Raw Images

Abstract: Splicing is a common image manipulation technique in which a region from a first image is pasted onto a second image to alter its content. In this paper, we use the fact that different images have different noise characteristics, according to the camera and lighting conditions during the image acquisition. The proposed method automatically detects image splicing in raw images by highlighting local noise inconsistencies within a quadtree scan of the image. The image noise is modelized by both Gaussian and Poisson noise components. We demonstrate the efficiency and robustness of our method on several images generated with an automated image splicing.

Measuring the Noise of Digital Imaging Sensors by Stacking Raw Images Affected by Vibrations and Illumination Flickering

Abstract: This paper discusses camera noise estimation from a series of raw images of an arbitrary natural static scene, acquired with the same camera settings. Although it seems natural to characterize noise from the random time fluctuation of pixel intensity, it turns out that these fluctuations may also be caused by illumination flickering and mechanical microvibrations affecting the camera. In this context, the contributions are twofold. First, a theoretical model of image formation in the presence of illumination flickering and of vibrations is discussed. This parametric model is based on a Cox process. It is shown that illumination flickering changes the standard affine relation between noise variance and average intensity to a quadratic relation. Second, under these conditions an algorithm is proposed to estimate the main parameters governing sensor noise, namely the gain, the offset, and the readout noise. The rolling shutter effect, which potentially affects the output of any focal-plane shutter camera, is...

A new filter to remove salt and pepper noise in color images

Abstract: In this paper, a new method is presented for reducing salt and pepper noise in color images. This method consists of three steps: In the first step, Laplacian operators and threshold values are used to identify pixels that are likely to have been corrupted by noise; in the second step, these noise candidates are judged by using the neighborhood of each pixel. After recognizing the noisy pixels, the vector median filter is used for replacing the noisy pixels, in the third step. The proposed algorithm is tested against different color images, and it gives a better peak signal-to-noise ratio and a lower normalized mean square error. These results have also been checked and analyzed visually. The performance of the proposed method is compared with common existing vector filters at different noise densities. Extensive experiments show that the proposed algorithm provides better performance than many of the existing vector filters in terms of noise suppression and preserving thin lines, fine details, and image edges.

A Denoising Method Based on Total Variation

Abstract: Today large amounts of digital images are created by various modern devices such as digital cameras, X-Ray scanners, and so on. Noise reduces image quality and result of the processing. For example, biomedical images are a type of digital images. In these images, there is a combination of two types of noises: Gaussian noise and Poisson noise. In this paper, we propose a method to remove these noises. This method is based on the total variation of an image intensity (brightness) function. We combine two famous denoising models to remove this combination of noises.

Quantile analysis of image sensor noise distribution

Abstract: This paper describes a study aimed at comparing the real image sensor noise distribution to the models of noise often assumed in image denoising designs. Quantile analysis in pixel, wavelet, and variance stabilization domains reveal that the tails of Poisson, signal-dependent Gaussian, and Poisson-Gaussian models are too short to capture real sensor noise behavior. Noise model mismatch would likely result in image denoising that undersmoothes real sensor data.

Sequentially combined mean-median filter for high density salt and pepper noise removal

Abstract: Salt and pepper noise removal is extremely challenging task when the noise density is very high in the corrupted image Usually the existing filters are not capable of removing this noise above 70% density This paper proposes a Sequentially Combined Mean-Median Filter (SCMMF) based on combination of arithmetic mean and median technique A binary flag image is generated with the help of identified noisy pixels which are first replaced by a value calculated by plus (+) operation with nearest pixels intensities The remaining noisy pixels are then replaced by neighbourhood mean operations Finally the image quality is enhanced by the median of the corresponding window Border operation is also incorporated to preserve the image size Experimental results show that SCMMF is outstanding from visual qualitative judgments for a number of standard images Moreover, this combined filter outperform over the existing with respect to MSE, PSNR, and SSIM comparison even at 92% noise density level

Detection of defects in a transparent polymer with high resolution tomography using white light scanning interferometry and noise reduction

Abstract: Transparent layers such as polymers are complex and can contain defects which are not detectable with classical optical inspection techniques. With an interference microscope, tomographic analysis can be used to obtain initial structural information over the depth of the sample by scanning the fringes along the Z axis and performing appropriate signal processing to extract the fringe envelope. By observing the resulting XZ section, low contrast, sub-μm sized defects can be lost in the noise which is present in images acquired with a CCD camera. It is possible to reduce temporal and spatial noise from the camera by applying image processing methods such as image averaging, dark frame subtraction or flat field division. In this paper, we present some first results obtained by this means with a white light scanning interferometer on a Mylar polymer, used currently as an insulator in electronics and micro-electronics. We show that sub-μm sized structures contained in the layer, initially lost in noise and barely observable, can be detected by applying a combination of image processing methods to each of the scanned XY images along the Z-axis. In addition, errors from optical imperfections such as dust particles on the lenses or components of the system can be compensated for with this method. We thus demonstrate that XZ section images of a transparent sample can be denoised by improving each of the XY acquisition images. A quantitative study of the noise reduction is presented in order to validate the performance of this technique.

On the robust technique of mixed Gaussian and impulsive noise reduction in color digital images

Abstract: In this paper a new technique of mixed Gaussian and impulsive noise suppression in color images is proposed. The novel approach is based on a weighted average of pixels contained in the filtering block. The main novelty of the proposed solution lies in the new definition of similarity between a pixel and samples belonging to a small window centered at the central pixel of the processing block. Instead of direct comparison of pixels, a measure based on the similarity between a given pixel and the samples from the window is utilized. This measure is defined as the sum of distances in a given color space between a pixel and a certain number of most similar pixels from this window. In this way, the new similarity measure is not influenced by the outliers injected into the image by the impulsive noise process and the averaging process ensures the effectiveness of the new filter in the reduction of Gaussian noise. The experimental results prove that the novel filtering design is capable of suppressing even strong mixed noise and is competitive with respect to state-of-the-art methods.

An efficient mixed noise removal technique from gray scale images using noisy pixel modification technique

Abstract: Removing or reducing noises from image is a very active research area in image processing domain. This paper presents an efficient noise removal technique to restore digital images corrupted by mixed noise, preserving image contents optimally. The proposed filtering technique consists of two steps: the noisy pixel detection step using fuzzy technique and the mixed noise filtering step. Noises addressed in this method are a combination of salt and pepper noise and Gaussian noise. This method reduces mixed noises considerably without compromising on edge sharpness. Experimental results show that the proposed technique consistently outperforms many existing fuzzy based algorithms while balancing the tradeoff between noise reduction and detail preservation. Hence, this mixed noise removal technique finds application in various segments of image processing like digital television, medical image processing, digital camera, surveillance systems etc.

Dynamic Simulation of Noise in 3D Infrared Scene Simulation

Abstract: Simulation of IR detector noise is a difficult part in engineering development of 3D dynamic IR scene simulation A technology of dynamically generating noise of IR detector is provided in this article First, by computing and analyzing the images sequence of IR detector, the random gauss noise, salt noise and heterogeneity noise is picked up In scene simulation developed by OSG, a noise "screen" is attached to the view of the simulation scene by technology of refer to texture (RTT), and the image of the heterogeneity noise is made as the RTT texture To realize the dynamic effect, the random gauss noise is generated and rendered by shader base on GPU The salt noise is directly rendered according to the sign of blind pixels in RTT texture The experiment shows that the noise simulated by technology in this article is lifelike, and the system cost of the technology is little The method in this paper provides a practical engineering train of thought for development of dynamic 3D IR scene

Development of mathematical morphology filter for medical image impulse noise removal

Abstract: Post-acquisition denoising of medical images is of importance for clinical diagnosis and computerized analysis, such as tissue classification and segmentation. During the image generation, imaging devices are quite often interfered by various noise sources. Impulse noise which causes the medical images to remove important image details such as edges, contours and texture. In this paper, a new filtering method is proposed to remove impulse noise on degraded medical images. The proposed filter is integrated with noise detector and filtering approach. An impulse noise detector using mathematical residues is proposed to identify pixels that are corrupted by impulse noise, and the image is recovered using specialized open-close algorithm that is only applied to the noisy pixels. Black and white blocks that degrade the quality of the image will be recovered by a block smart erase method. The proposed method was tested on simulated medical images from a brain web database and clinical medical images with different levels of noise. The results show that the morphology filter produces better denoising results in terms of qualitative and quantitative measures compared with other denoising methods, compared with several existing noise filtering models demonstrated that not only the proposed filter is effective for noise removal but also for image detail preservation and clinical practice.

A Radon-Transform-Based Image Noise Filter—With Applications to Multibeam Bathymetry

Abstract: This paper describes a linear-image-transform-based algorithm for reducing stripe noise, track line artifacts, and motion-induced errors in remote sensing data. Developed for multibeam bathymetry (MB), the method has also been used for removing scalloping in synthetic aperture radar images. The proposed image transform is the composition of an invertible edge detection operator and a fast discrete Radon transform (DRT) due to Gotz, Druckmuller, and Brady. The inverse DRT is computed by using an iterative method and exploiting an approximate inverse algorithm due to Press. The edge operator is implemented by circular convolution with a Laplacian point spread function modified to render the operator invertible. In the transformed image, linear discontinuities appear as high-intensity spots, which may be reset to zero. In MB data, a second noise signature is linked to motion-induced errors. A Chebyshev approximation of the original image is subtracted before applying the transform, and added back to the denoised image; this is necessary to avoid boundary effects. It is possible to process data faster and suppress motion-induced noise further by filtering images in nonoverlapping blocks using a matrix representation for the inverse DRT. Processed test images from several MB data sets had less noise and distortion compared with those obtained with standard low-pass filters. Denoising also improved the accuracy in statistical classification of geomorphological type by 10–28% for two sets of invariant terrain features.

A decision based switching median filter for restoration of images corrupted by high density impulse noise

Abstract: Digital images are corrupted by impulse noise mainly due to sensor faults of image acquisition devices and adverse channel environment which in turn degrades the image quality. A decision based switching median filter (DBSMF) to restore images corrupted with high density impulse noise is proposed in this paper. The global use of standard median filters for impulse noise removal from corrupted images provide good results but the filtering operation may affect fine pixels in addition to noisy pixels which leaves a blurred effect on the filtered image. In order to address this issue the proposed algorithm makes use of an efficient detection scheme to identify the noise pixels and noise free pixels. The detection algorithm clusters the pixels in the corrupted image so as to fall under three categories which states whether the pixels are corrupted or uncorrupted. The proposed switching median filter processes only on those pixels that are classified as corrupted and replaces the processing pixel by the median value. Under high noise densities the filtering window consists of more number of corrupted pixels. For such cases, the proposed algorithm restricts certain conditions on the expansion of the filtering window size to effectively choose the median value. The performance of this decision based algorithm is tested against four noise models for different levels of noise densities and is evaluated in terms of performance metrics which include Peak Signal to Noise ratio (PSNR) and Image Enhancement Factor (IEF). It gives better results for images that are extremely corrupted up to 90% noise density and outperforms classic filters in terms of handling image corruption.

Noise reduced high dynamic range tone mapping using information content weights

Abstract: In this paper, we propose a noise reduced tone mapping method based on information content weights, where the perceptually unimportant pixels are smoothed during the decomposition in two steps First, a saliency-based information content weight is introduced to give high fidelity to the data term based on the ratio of the local pixel power and the overall noise power in the base layer decomposition Then, the detail layer is subtracted using the mutual information-based information content weight from the original image luminance and the clean base layer Experiments show the effectiveness of the proposed method in the improvements of both signal-to-noise ratio and visual quality

No-reference image quality assessment for removal of processed and unprocessed noise

Abstract: We present a fast no-reference quality measurement method based on the entropy analysis of the image structure. The information that an images carries is represented not only by intensity values but also their position in the image. We examine the behavior of the entropy by altering the positions to distinguish the image structure, noise and blur. We propose a method to rearrange the pixel placements using a random pixel scattering transform. Using this transform we firstly determine the local power of image structure relative to noise and then we obtain the quality index by combining this information with our proposed blur factor. We show that this method is useful to select denoising parameters automatically in both unprocessed (white Gaussian) and processed (frequency-dependent) noise when the reference image is not available. Our method is easy to implement and yet rivals state-of-the-art quality measurement approaches.