Showing papers on "Dark-frame subtraction published in 2009"

Signal-dependent noise modelling and estimation of new-generation imaging spectrometers

Abstract: This paper deals with an original method suitable for estimating the noise introduced by optical imaging systems, such as CCD cameras, multispectral scanners and imaging spectrometers. The power of the signal-dependent photonic noise is decoupled from that of the signal-independent noise generated by the electronic circuitry. The method relies on the multivariate regression of local sample mean and variance. Statistically homogeneous pixels produce scatter-points that are clustered along a straight line, whose slope and intercept measure the signal-dependent and signal-independent components of the noise power, respectively. Experimental results carried out on a simulated noisy image and on true data from a modern generation airborne imaging spectrometer highlight the accuracy of the proposed method and its robustness to textures that may lead to a gross over-estimation of the noise, for high SNR.

Design and Development of an Improved Adaptive Median Filtering Method for Impulse Noise Detection

Abstract: An Impulse noise detection and removal with adaptive filtering approach is proposed to restore images corrupted by salt & pepper noise. The proposed algorithm works well for suppressing impulse noise with noise density from 5 to 60% while preserving image details. The difference of current central pixel with median of local neighborhood pixels is used to classify the central pixel as noisy or noise-free. The noise is attenuated by estimating the values of the noisy pixels with a switching based median filter applied exclusively to those neighborhood pixels not labeled as noisy. The size of filtering window is adaptive in nature, and it depends on the number of noise-free pixels in current filtering window. Simulation results indicate that this filter is better able to preserve 2-D edge structures of the image and delivers better performance with less computational complexity as compared to other denoising algorithms existing in literature.

Source Camera Identification Using Large Components of Sensor Pattern Noise

Abstract: Digital image forensics has attracted a lot of attention recently for its role in identifying the origin of digital image. Although different forensic approaches have been proposed, one of the most popular approaches is to rely on the imaging sensor pattern noise, where each sensor pattern noise uniquely corresponds to an imaging device and serves as the intrinsic fingerprint. The correlation-based detection is heavily dependent upon the accuracy of the extracted pattern noise. In this work, we discuss the way to extract the pattern noise, in particular, explore the way to make better use of the pattern noise. Unlike current methods that directly compare the whole pattern noise signal with the reference one, we propose to only compare the large components of these two signals. Our detector can better identify the images taken by different cameras. In the meantime, it needs less computational complexity.

An Integrating Wide Dynamic-Range Image Sensor With a Logarithmic Response

Abstract: An integrating CMOS image sensor with a wide dynamic range is described. The dynamic range of these pixels is controlled by a user-defined reference voltage that creates a photocurrent-dependent effective integration time. The operation of these pixels and a method of obtaining a well-controlled logarithmic response are both described. Furthermore, described are the results of two alternative methods of correcting the fixed pattern noise in these pixels and measurements of the temporal noise from individual pixels. These results show that with these pixels, it is possible to match the contrast sensitivity of the human visual system.

Peformance evaluation of various speckle noise reduction filters on medical images

Abstract: 1Reader, Punjabi University,Patiala-147002(Punjab), India, E-Mailresearch_raman@yahoo.com 2Reader Punjabi University, Patiala-147002(Punjab), India E-mail:himagrawal@rediffmail.com Abstract-Today ultrasound and magnetic resonance imaging are essential tools for noninvasive medical diagnosis. One of the fundamental problems in this field is speckle noise, which is a major limitation on image quality especially in ultrasound imaging. The presence of the speckle noise affects image interpretation by human and the accuracy of computer-assisted diagnostic techniques. Low image quality is an obstacle for effective feature extraction, analysis, recognition and quantitative measurements. Image variances or speckle is a granular noise that inherently exists in and degrades the quality of the medical images. Before using ultrasound and magnetic resonance imaging, the very first step is to reduce the effect of Speckle noise. Most of speckle reduction techniques have been studied by researchers; however, there is no comprehensive method that takes all the constraints into consideration. Filtering is one of the common methods which is used to reduce the speckle noises. This paper compares five different speckle reduction filters quantitatively using simulated imageries. The results have been presented by filtered images, statistical tables and diagrams. Finally, the best filter has been recommended based on the statistical and experimental results.

Reducing digital image noise

Abstract: Devices, systems, methods, and other embodiments associated with reducing digital image noise are described. In one embodiment, a method includes determining, on a per pixel basis, mosquito noise values associated with pixels of a digital image. The method determines, on a per pixel basis, block noise values associated with the digital image. The method filters the digital image with a plurality of adaptive filters. A compression artifact in the digital image is reduced. The compression artifact is reduced by combining filter outputs from the plurality of adaptive filters. The filter outputs are combined based, at least in part, on the mosquito noise values and the block noise values.

Image processing device, image sensing apparatus, and image processing method

Abstract: An image processing device is comprised of: a frequency component resolution section for resolving an image obtained from an image sensor having a light-shielded pixel area and a non-light-shielded pixel area into two or more frequency components; a noise amount calculation section for calculating a noise amount for the frequency component based on the frequency component in the light-shielded pixel area; a noise suppression section for suppressing the noise component for the frequency component in the non-light-shielded pixel area according to the noise amount that has been calculated by the noise amount calculation section; and a frequency component synthesis section for synthesizing the frequency component that has been resolved by the frequency component resolution section to thereby form an image.

Spectral sensitivity optimization of color image sensors considering photon shot noise

Abstract: Filter optimization is investigated to design digital camera color filters that achieved high color accuracy and low image noise when a sensor's inherent photon shot noise is considered. In a computer simulation, both RGB- and CMY-type filter sets are examined. Although CMY filters collect more photons, performance is worse than for RGB filters in terms of either color reproduction or noise due to the large noise amplification during the color transformation. When RGB filter sets are used and photon shot noise is considered, the peak wavelength of the R channel should be longer (620 to 630 nm) than the case when only color reproduction is considered: peak wavelengths 600, 550, and 450 nm for RGB channels, respectively. Increasing the wavelength reduces noise fluctuation along the a* axis, the most prominent noise component in the latter case; however, color accuracy is reduced. The tradeoff between image noise and color accuracy due to the peak wavelength of the R channel leads to a four-channel camera consisting of two R sensors and G and B. One of the two R channels is selected according to the difference in levels to reduce noise while maintaining accurate color reproduction.

Non-linear dark current fixed pattern noise compensation for variable frame rate moving picture cameras

Abstract: CMOS image sensors are used in most of the camera systems today. For achieving a high image quality it is essential to compensate for fixed pattern noise. Compensation can be carried out by subtracting an estimated noise value per pixel, either directly on the sensor or in the digital processing. Unfortunately these values are different for each camera and will vary for different exposure times, camera mode settings and temperature. This poses additional challenges for high-end moving picture camera systems. We present a new algorithm for improved fixed pattern noise compensation that extends the currently available linear models. Measurements of a real world camera system and a simulation are used to show the improvements with our algorithm. Significant improvement of the compensated fixed pattern noise over a wide exposure range is shown. This allows the operation of the camera system at a much wider range of frame rates and especially long exposures are now possible. Our algorithm can be implemented without increasing the required memory bandwidth which saves power, size and cost.

The Effect of Motion Blur and Signal Noise on Image Quality in Low Light Imaging

Abstract: Motion blur and signal noise are probably the two most dominant sources of image quality degradation in digital imaging. In low light conditions, the image quality is always a tradeoff between motion blur and noise. Long exposure time is required in low illumination level in order to obtain adequate signal to noise ratio. On the other hand, risk of motion blur due to tremble of hands or subject motion increases as exposure time becomes longer. Loss of image brightness caused by shorter exposure time and consequent underexposure can be compensated with analogue or digital gains. However, at the same time also noise will be amplified. In relation to digital photography the interesting question is: What is the tradeoff between motion blur and noise that is preferred by human observers? In this paper we explore this problem. A motion blur metric is created and analyzed. Similarly, necessary measurement methods for image noise are presented. Based on a relatively large testing material, we show experimental results on the motion blur and noise behavior in different illumination conditions and their effect on the perceived image quality.

An improved camera identification method based on the texture complexity and the image restoration

Abstract: The identification of source camera is useful to improve the capability of evidence in the digital image such as distinguish the photographer taking illegal images and adopting digital images as evidence of crime. Lukas, et al. showed the method for source camera identification based on the correlation of PNU (pixel nonuniformity) noise. However, the wavelet-based denoising filter for suppressing the random noise reduces the accuracy of camera identification. It is caused by the fact that the denoising filter diffuses the edge and makes the PNU noise less pronounced. Moreover, it is difficult to extract PNU noise from the images taken by cameras which are equipped with the image improvement functions such as motion blur correction, contrast enhancement, and noise reduction. In this paper, we propose a method for improving the camera identification accuracy by selecting pixels based on the texture complexity. We also propose a-method for improving the identification accuracy by applying the image restoration method.

Image capturing system and computer readable recording medium for recording image processing program

Abstract: An image capturing system includes: a first noise reduction unit which roughly removes the effects of an edge component by performing edge-preserving adaptive noise reduction processing on a target pixel within a local region including the target pixel and the neighboring pixels extracted from an image signal acquired from a CCD; a noise estimation unit which dynamically estimates the noise amount with respect to the target pixel based upon the target pixel value thus subjected to the noise reduction processing by the first noise reduction unit; and a second noise reduction unit which performs noise reduction processing on the target pixel based upon the target pixel value thus subjected to the noise reduction processing by the first noise reduction unit and the noise amount thus estimated by the noise estimation unit.

Method for reducing row noise with dark pixel data

Abstract: A method for reducing the row noise from complementary metal oxide semiconductor (CMOS) image sensor by using average values from sub-regions of the shielded pixels. The method operates on sensor with and without a Color Filter Array (CFA) before any interpolation is applied and estimates the local offset by subtracting out outliers and averaging the averages of sub-regions in the shielded pixels. The method also reduces the pixel-to-pixel noise while reducing the row noise.

Interaction of image noise, spatial resolution, and low contrast fine detail preservation in digital image processing

Abstract: We present a method to improve the validity of noise and resolution measurements on digital cameras. If non-linear adaptive noise reduction is part of the signal processing in the camera, the measurement results for image noise and spatial resolution can be good, while the image quality is low due to the loss of fine details and a watercolor like appearance of the image. To improve the correlation between objective measurement and subjective image quality we propose to supplement the standard test methods with an additional measurement of the texture preserving capabilities of the camera. The proposed method uses a test target showing white Gaussian noise. The camera under test reproduces this target and the image is analyzed. We propose to use the kurtosis of the derivative of the image as a metric for the texture preservation of the camera. Kurtosis is a statistical measure for the closeness of a distribution compared to the Gaussian distribution. It can be shown, that the distribution of digital values in the derivative of the image showing the chart becomes the more leptokurtic (increased kurtosis) the stronger the noise reduction has an impact on the image.

FPGA implementation of Real Time acoustic noise suppression by Spectral Subtraction using Dynamic Moving Average Method

Abstract: The Paper presents the outlines of the Field Programmable Gate Array (FPGA) implementation of Real Time speech enhancement by Spectral Subtraction of acoustic noise using Dynamic Moving Average Method. It describes an stand alone algorithm for Speech Enhancement and presents a architecture for the implementation. The traditional Spectral Subtraction method can only suppress stationary acoustic noise from speech by subtracting the spectral noise bias calculated during non-speech activity, while adding the unique option of dynamic moving averaging to it, it can now periodically upgrade the estimation and cope up with changes in noise level. Signal to Noise Ratio (SNR) has been tested at different noisy environment and the improvement in SNR certifies the effectiveness of the algorithm. The FPGA implementation presented in this paper, works on streaming speech signals and can be used in factories, bus terminals, Cellular Phones, or in outdoor conferences where a large number of people have gathered. The Table in the Experimental Result section consolidates our claim of optimum resouce usage.

Noise reduction using multi-resolution edge detection

Abstract: In this paper, a new noise reduction algorithm is proposed. In general, an edge-high frequency information in an image-would be filtered or suppressed after image smoothing. The noise would be attenuated, but the image would lose its sharp information. This defect makes the post-processing harder. One new algorithm performs connectivity analysis on edge-data to make sure that only isolated edge information that represents noise gets filtered out, hence preserving the overall edge structure of the original image. The steps of new algorithm are as follows. First, find the edge from the noisy image by multi-resolution analysis. Second, use connectivity analysis to direct a mean filter to suppress the noise while preserving the edge information. In the first step, we propose a new algorithm to find edges in a very noisy image. The algorithm is based on the analysis of a group of multi-resolution images obtained by processing the original noisy image by different Gaussian filters. After applied to a sequence of images of the same scene but with different signal-noise-ratio (snr), this method is robust to remove noise and keep the edge. Also, through statistic analysis, there exists the regularity that the parameters of the algorithm would be constant with varying images under the same snr.

Denoising scheme for realistic digital photos from unknown sources

Abstract: This paper targets denoising of digital photos taken by cameras with unknown sensor parameters and image processing pipeline. Common noise characteristics in such images originate from camera-internal processing, such as demosaicing, tone mapping, and JPEG compression. Three of the noise characteristics that are not adequately addressed by existing denoising algorithms are spatially correlated low-frequency noise, strong signal dependency of the noise level and high levels of the chrominance noise relative to the luminance noise. We propose a generic scheme that extends existing denoisers such as the bilateral filter to account for all the problems above. Our solution combines a novel progressive pyramidal filtering scheme to address the correlated noise, filter adaptation via local noise level estimation and luminance-guided chrominance filtering to address the low-SNR of the chrominance channels. We demonstrate the effectiveness of our solution for challenging realistic noisy photos.

Imaging system, image processing method, and image processing program

Abstract: A favorable noise reduction process that is optimized for capturing conditions and that prevents the occurrence of residual image components is enabled. Provided is an imaging system including: a first extraction section that extracts a local region that includes a pixel of interest from an image signal; a second extraction section that extracts, from another image signal captured at a different time, a local region located at almost the same position as said local region; a first noise reduction section that performs a noise reduction process by using the local regions; a noise estimation section that estimates an amount of noise included in the pixel of interest; a residual image detection section that detects a residual image component included in the local region based on the estimated amount of noise; and a second noise reduction section that performs a noise reduction process based on the detected residual image component.

Influence of illumination on dark current in charge-coupled device imagers

Abstract: Thermal excitation of electrons is a major source of noise in charge-coupled-device (CCD) imagers. Those electrons are gen- erated even in the absence of light, hence, the name dark current. Dark current is particularly important for long exposure times and elevated temperatures. The standard procedure to correct for dark current is to take several pictures under the same condition as the real image, except with the shutter closed. The resulting dark frame is later subtracted from the exposed image. We address the ques- tion of whether the dark current produced in an image taken with a closed shutter is identical to the dark current produced in an expo- sure in the presence of light. In our investigation, we illuminated two different CCD chips with different intensities of light and measured the dark current generation. A surprising result of this study is that some pixels produce a different amount of dark current under illumi- nation. Finally, we discuss the implication of this finding for dark frame image correction. © 2009 SPIE and IS&T.

Image processing apparatus, image processing method, and computer readable storage medium which stores image processing program

Abstract: An image processing apparatus includes a noise reduction unit which performs noise reduction processing to image signals, a first noise presumption unit which presumes a first noise amount from a present image signal among the image signals, and a second noise presumption unit which presumes a second noise amount based on the first noise amount, the present image signal, and the image signal of the past which underwent the noise reduction processing. The noise reduction unit performs the noise reduction processing to the present image signal based on the second noise amount.

Integrated Noise Modeling for Image Sensor Using Bayer Domain Images

Abstract: Most of image processing algorithms assume that an image has an additive white Gaussian noise (AWGN). However, since the real noise is not AWGN, such algorithms are not effective with real images acquired by image sensors for digital camera. In this paper, we present an integrated noise model for image sensors that can handle shot noise, dark-current noise and fixed-pattern noise together. In addition, unlike most noise modeling methods, parameters for the model do not need to be re-configured depending on input images once it is made. Thus the proposed noise model is best suitable for various imaging devices. We introduce two applications of our noise model: edge detection and noise reduction in image sensors. The experimental results show how effective our noise model is for both applications.

Method And System For Motion Compensated Noise Level Detection And Measurement

Abstract: Two consecutive interlaced video pictures of the same polarity or two consecutive progressive video pictures are read by a video processing system. The video pictures may comprise a current picture and a noise reduced reference picture. Motion and/or motion vectors may be estimated between the current and reference pictures by a motion compensated noise detector and/or a motion compensated temporal filter. A noise level sample may be determined for a pixel in the current picture based on a window of pixel data from the current picture and a window of motion compensated pixel data from the reference picture. One or more of a moving edge gradient value, a moving content value and a determined range of noise level values may be utilized to determine a valid noise sample. Noise level samples may be accumulated and a noise level may be determined for the current picture.

Noise Estimation and Reduction in Microarray Images

Abstract: Lots of error sources affect the microarray image quality, especially the noise. An image may contain different type noises which will produce distinct influence on image processing, so it doesn’t need to remove all. This paper analyzed the affection of different noises to automatic gridding and proposed grid line number for quantitive evaluation. A new algorithm for noise reduction was developed, which included two parts: edge noise reduction and highly fluorescence noise reduction. Edge detection was executed on the vertical and horizontal projections of microarray image. Highly fluorescent noise was removed by linear replace, which is an easy and fast means. The algorithm was implemented and compared to other common noise reduction methods. Experiment results show the feasibility of the proposed approach.

A Two-Pass Filter for Impulse Noise Reduction Based on Edge Characteristics

Abstract: In this paper, a two-pass filter is proposed for removing impulse noise to improve image quality. Due to the fact that the edge is more obvious and detected easily, such characteristics of different edges are used to judge whether the pixel is on an edge. Then, the pixels are not on the edges will be filtered by median filter, and further, the other pixels will be detected to preserve original information and reduce impulse noise. Experimental results show that the proposed method is more effective for impulse noise reduction to improve image quality; especially the corruption ratio above 20%.

Enhancing Salt-and-Pepper Noise Removal in Binary Images of Engineering Drawing

Abstract: Noise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms.

Evolving Image Noise Filters through Genetic Programming

Abstract: A form of Evolutionary Computation (EC) called Genetic Programming (GP) was used to automatically discover sequences of image noise filters to remove two types of image noise and a type of communications noise associated with a remotely sensed imagery. Sensor noise was modeled by the addition of salt-and-pepper and grayscale noise to the image. Communication noise was modeled by inserting a series of blank pixels in selected image rows to replicate dropped pixel segments occurring during communication interruptions of sequential uncompressed image information. A known image was used for training the evolver. Heavy amounts of noise were added to the known image, and a filter was evolved. (The filtered image was compared to the original with the average image-toimage pixel error establishing the fitness function.). The evolved filter derived for the noisy image was then applied to never-before-seen imagery affected by similar noise conditions to judge the universal applicability of the evolved GP filter. Examples of all described images are included in the presentation. A variety of image filter primitives were used in this experiment. The evolved sequences of primitives were each then sequentially applied to produce the final filtered image. These filters were evolved over a typical run length of one week each on a small Linux cluster. Once evolved, the filters were then transported to a PC for application to the never-before-seen images, using an “evolve-once, apply-many-times” approach. The results of this image filtering experiment were quite dramatic.