Showing papers on "Dark-frame subtraction published in 2007"

Noise Characteristics of 3D Time-of-Flight Cameras

Abstract: Time-of-flight (TOF) cameras are based on a new technology that delivers distance maps by the use of a modulated light source. In this paper we first describe a set of experiments that we performed with TOF cameras. We then propose a noise model which is able to explain some of the phenomena observed in the experiments. The model is based on assuming a noise source that is correlated with the light source (shot noise) and an additional additive noise source (dark current noise). The model predicts well the dependency of the distance errors on the image intensity and the true distance at an individual pixel.

An efficient algorithm for the removal of impulse noise from corrupted images

Abstract: In this letter, we propose an efficient algorithm, which can successfully remove impulse noise from corrupted images while preserving image details. It is efficient, and requires no previous training. The algorithm consists of two steps: impulse noise detection and impulse noise cancellation. Extensive experimental results show that the proposed approach significantly outperforms many other well-known techniques for image noise removal.

Apparatus and method for image pickup

Abstract: An apparatus and method for image pickup capable of reducing noise in the images to reduce the effect of shaking due to, for example, hand movement. The image pickup apparatus and method can employ an exposure photographing unit for photographing at least one exposure image at predetermined time intervals using time-division exposure in a light exposure state, a dark frame photographing unit for photographing at least one dark frame at predetermined time intervals using time-division exposure in a dark state, a subtracting processor for subtracting the dark frames from the exposure images to reduce noise in the exposure images, and an image combiner for combining the plurality of reduced noise exposure images.

Imaging device, image processing device, image processing method, program for image processing method, and recording medium having program for image processing method recorded thereon

Abstract: A method and apparatus to remove color noise included in raw data while effectively preventing image quality degradation. For Interest pixels serially set onto a mosaic image formed of raw data, conversion is executed into a pixel value for noise removal based on a processing reference pixel value having a unified color signal component in each interest pixel, noise is removed from the pixel value for noise removal, and the pixel value for noise removal with noise removed is converted into the source pixel value, whereby only color noise can be removed without affecting a luminance signal.

Reducing noise in digital images

Abstract: A target digital image is received from an image sensor. The image is contaminated by noise of unknown magnitude that is represented by a reference digital image. A process is applied that uses statistical analysis of the target digital image and of the reference digital image to estimate a magnitude of the noise for at least some pixels of the target digital image.

Method of and apparatus for eliminating image noise

Abstract: A method and apparatus are provided for eliminating image noise to remove spatial-temporal noise and improve visibility. The method includes extracting a spatial-temporal noise level of neighbor pixels around a current pixel, filtering noise of the current pixel by applying a weight to spatial-temporal pixels around the current pixel based on the extracted spatial-temporal noise level, and applying a weight to the noise-filtered pixel and a boosted-up pixel based on an edge intensity and summing the weight-applied pixels. The spatial-temporal noise level is extracted based on spatial-temporal information of neighbor pixels around a current pixel in a current frame and spatial-temporal information of neighbor pixels around a current pixel in a previous frame.

Image sensor device having improved noise suppression capability and a method for suppressing noise in an image sensor device

Abstract: An image sensor device is provided that has an uncovered imaging array of pixels and a covered global reference non-imaging array of pixels. The pixel samples of the global reference non-imaging array are used to remove noise from the pixel samples of the imaging array. The control signals and control lines that are used to sample the pixels of the imaging array are separate from and independent of the control signals and control lines that are used to sample the pixels of the global reference non-imaging array of pixels. For each row of pixels of the imaging array that is sampled, the same row of pixels of the global reference non-imaging array is sampled. The global reference row has no or very few offsets or variations to ensure that noise removal is performed effectively.

Modeling and real-time estimation of signal-dependent noise in quantum-limited imaging

Abstract: Many established and emerging image processing applications rely on quantum-limited imaging, i.e., imaging in extremely poor illumination. At this, images are corrupted by severe signal-dependent Poisson noise. For optimal noise reduction the noise characteristics must be estimated and integrated into the method. Common noise estimators, however, assume Gaussian noise which is not signal-dependent. In this paper, we describe the modeling process exemplarily for low-dose medical X-ray imaging. In this context, we formulate functional models for detector images and images which have undergone nonlinear white compression prior to further processing. Furthermore, we present a robust estimator for signal-dependent noise suited for real-time applications.

Digital filtering with noise gain limit

Abstract: A method for producing a camera (20), which includes objective optics (22) for forming an image on an electronic image sensor (24) and a digital filter (26) for filtering an output of the image sensor. The method includes defining a maximum permissible value of a noise gain and determining one or more aberrations due to the objective optics. Coefficients of the digital filter are calculated so as to compensate for the one or more aberrations while preventing the noise gain of the digital filter from exceeding the maximum permissible value. The output of the image sensor is filtered using the computed coefficients so as to generate an enhanced output image.

Electronic image capture with reduced noise

Abstract: The present invention relates to an electronic image capture system for capturing an electronic image having reduced noise, and a method for capturing an electronic image having reduced noise, for example from a digital camera device. An electronic image capture system (1) for capturing a reduced noise image of a scene (2) comprises a detector array (8) and an image processing apparatus (10). The detector array (8) is arranged to provide to the image processing apparatus (10) data (18) representing at least one image (6) of a scene detected by the array (8), and the image processing apparatus holds a noise model that substantially characterises the noise performance of the image capture system (1). The image processing apparatus (10) is arranged to identify, using the image data (18) and the noise model, one or more portions of the scene (2) that would contribute disproportionately to visible noise in an image formed from said image data (18), and to select an exposure pattern on the basis that said selected exposure pattern will reduce the contribution to the visible noise when exposures from said selected exposure pattern are combined to form the reduced noise image.

Adaptive Window Based Efficient Algorithm for Removing Gaussian Noise in Gray Scale and Color Images

Abstract: In this paper a new efficient algorithm for the removal of Gaussian noise in gray scale and color images using adaptive window is presented. The function of the algorithm is to replace each corrupted pixel by a mean value of the pixels inside an adaptive window. The adaptive window is formed using a threshold calculated form noise variance. The proposed algorithm is simple and it works very effectively in removing Gaussian noise compare with the other techniques. The proposed algorithm is tested for both gray scale and color images corrupted with Gaussian noise. The visual and quantitative results show that the proposed algorithm performs well in removing Gaussian noise and preserve edge details.

Noise reduction apparatus, method and program for controlling same, image sensor and digital camera

Abstract: Image data pixels indicative of the pixels in a noise-reduction target area having a size of 5×5 pixels is extracted from a plurality of types of CCD-RAW data having red, green and blue color components. A filter for reducing uncorrelated noise is calculated. Uncorrelated noise is removed by performing a filter operation using the calculated filter while correlativity of the CCD-RAW data is maintained. These processing steps are repeated for one frame of CCD-RAW data. After uncorrelated noise has been removed, spatial pixel processing such as an aperture correction is applied.

A Detail-Preserving Type-2 Fuzzy Logic Filter for Impulse Noise Removal from Digital Images

Abstract: A novel filtering operator based on type-2 fuzzy logic techniques is proposed for detail preserving restoration of impulse noise corrupted images. The performance of the proposed operator is tested for different test images corrupted at various noise densities and also compared with representative conventional as well as state-of-the-art impulse noise removal operators from the literature. Experimental results show that the proposed operator exhibits superior performance over the competing operators and is capable of efficiently suppressing the noise in the image while at the same time effectively preserving the useful information in the image.

CMOS Image Sensor Noise Reduction Method for Image Signal Processor in Digital Cameras and Camera Phones

Abstract: Digital images captured from CMOS image sensors suffer Gaussian noise and impulsive noise. To efficiently reduce the noise in Image Signal Processor (ISP), we analyze noise feature for imaging pipeline of ISP where noise reduction algorithm is performed. The Gaussian noise reduction and impulsive noise reduction method are proposed for proper ISP implementation in Bayer domain. The proposed method takes advantage of the analyzed noise feature to calculate noise reduction filter coefficients. Thus, noise is adaptively reduced according to the scene environment. Since noise is amplified and characteristic of noise varies while the image sensor signal undergoes several image processing steps, it is better to remove noise in earlier stage on imaging pipeline of ISP. Thus, noise reduction is carried out in Bayer domain on imaging pipeline of ISP. The method is tested on imaging pipeline of ISP and images captured from Samsung 2M CMOS image sensor test module. The experimental results show that the proposed method removes noise while effectively preserves edges.

Image processing apparatus, imaging apparatus, image processing method, and image processing program

Abstract: PROBLEM TO BE SOLVED: To provide an image processing apparatus capable of executing effective noise elimination with less blurred edges, an imaging apparatus, an image processing method, and an image processing program. SOLUTION: An image information acquisition section 101 acquires image information, a component separation section 102 separates the acquired image information into luminance information and chrominance information, a luminance component noise elimination section 105 eliminates noise of the luminance information by using a first noise elimination method, and a chrominance component noise elimination section 106 eliminates noise of the chrominance information by using a second noise elimination method different from the first noise elimination method employed by the luminance component noise elimination section 105. COPYRIGHT: (C)2007,JPO&INPIT

Noise reduction device, noise reduction method, and electronic equipment

Abstract: PROBLEM TO BE SOLVED: To provide a noise reduction device using two-dimensional noise reduction processing as well as three-dimensional noise reduction processing, which is capable of reducing calculation errors of movement amounts of respective pixels due to an influence of noise. SOLUTION: A noise reduction device 30 includes: a two-dimensional noise reduction processing part 32 which subjects an image signal inputted from the outside to two-dimensional noise reduction processing; a movement detection part 33 which obtains movement amounts in a time base direction of respective pixels by calculating a difference value between a signal after two-dimensional noise reduction processing and a noise-reduced signal of one frame before; and a feedback coefficient and synthesis coefficient detection part 34 which adjusts a gain of three-dimensional noise reduction processing, to which the image signal inputted from the outside should be subjected, in accordance with movement amounts in the time base direction of respective pixels. COPYRIGHT: (C)2009,JPO&INPIT

Noise and Signal Activity Maps for Better Imaging Algorithms

Abstract: In this work, we propose noise and signal activity estimation method that discriminates noise from signal based on local and global properties of the image data. The method yields pixel-wise maps of the noise variance and of the signal activity. Using these maps to guide imaging algorithms such as image enhancement and print defect detection improves their performance. The proposed method does not assume a white Gaussian noise model; it is very efficient computationally and, as such, is useful for a wide variety of applications.

Noise Reduction in CMOS Image Sensors for High Quality Imaging: The Autocorrelation Function Filter on Burst Image Sequences

Abstract: We propose a new method for image noise detection and reduction in complementary metal oxide semiconductor (CMOS) image sensors inspired from audio noise cancelling techniques. Our algorithm is based on computing efficiently the time-dependent pixel autocorrelation function (ACF) from constant time interval acquired sequences of images. We demonstrate the effectiveness of our approach for successfully detecting and reducing white noise. Further, we consider an adaptive filter that exhibits significant computational improvements making it highly practical. Finally, we report on experiments displaying the highquality imaging systems obtained in practice.

Noise Detection and Reduction for Image Sensor by Time Domain Autocorrelation Function Method

Abstract: The present paper describes noise detection and reduction for an imaging system by a method of autocorrelation of pixel data change as a function of time. The random noise level could be detected for each pixel using the proposed method. An algorithm proposed for determining the noise level, and calculating the pixel value by the autocorrelation function value reduced the calculation cost. The proposed method is effective for reducing noise in real time image processing.

Noise Analysis and the Study of SNR Model on the Astronomical CCD Camera

Abstract: Three primary kinds of noise in the astronomical CCD camera system were analyzed: photon noise,readout noise and dark current noise,etc.Then the SNR model under the low-light-level conditions is proposed and further the simplified models vs different exposure time are proposed.According to the different simplified models,two different noise dominant regions are specified,photon-noise dominant region and readoutnoise dominant region.In the interest of the higher SNR and detecting ability in the CCD image,the on-chip binning technology can be employed.The experiment result indicates that the SNR model and the simplified models which be proposed out in this paper is effective.Therefore,the different exposure strategy can be adopted to detect the targets in the images effectively under different application.

Method for estimating noise

Abstract: An image noise detection method is disclosed. The image noise detection method includes the following steps: obtaining a spatial information of an image; obtaining a temporal information of the image; and determining a spatial noise or a temporal noise of the image according to both the spatial information, and the temporal information.

Line noise eliminating apparatus, line noise eliminating method, and line noise eliminating program

Abstract: To provide a line noise eliminating apparatus and the like, with which the picture quality of the area that has no line noise is not deteriorated, and a line noise having no periodicity can be eliminated. The line noise eliminating apparatus includes: an image binarizing device which generates a binary image by an input image; a line noise reliability calculating device which calculates an edge feature quantity for each of black-pixel consecutive areas in the rotated images, and calculates line noise reliability based on the edge feature quantities; a line noise area determining device which determines the line noise areas that correspond to each of the rotation angle candidates based on the line noise reliability; a density converting device which generates a density-converted image by applying local image enhancement on an area that corresponds to the line noise area of the input image so as to generate a density-converted image.

Quantum Noise Suppression in X-ray Images using Fuzzy 2-D Wiener Filter

Abstract: The noisy images are caused by decreasing quantity of the produced X-ray due to the deformation of the X-ray tube's anode. While obtaining the image from low quantity X-ray, shot noise or quantum noise occurs, and this decreases the quality of the image. The aim of this study is to define the novel method called as Fuzzy 2-D Weiner filter (FWF-2D), which suppresses the shot noise from noisy image by avoiding any harm to the image details. With this filtering technique, it is possible to preserve the sharp-edge and the details of the image without any damage during suppressing the noise from images. FWF-2D is one of the best techniques to suppress the corrupted quantum noises due to damaged X-ray tube. The fuzzy rules used in this filter are aimed to distinguish noise pixels from image ones, and the Wiener Filter is working to remove noise pixels distinguished by these fuzzy rules. It is then possible to obtain clean images from damaged X-ray tubes by using FWF-2D technique.

Image sensor noise reduction

Abstract: An imaging system includes a plurality of pixels. A pixel readout circuit produces a plurality of first image frames from those pixels. An image output circuit produces a plurality of second image frames and operates to produce a second image frame from more than one of the first image frames. The pixel readout circuit is enabled to produce the first images frames at a rate faster than the image output circuit produces the second image frames. Through combining first image frames, by averaging or other statistical combinations, the photon shot noise of second image frames is reduced. Photon shot noise affects images with high light levels more than those with low light levels and, as such, the system processing alters the rate of first image frames dependent on the current light levels.

Application of SUSAN to reduce the speckle noise of remote sensing image

Abstract: Speckle noise can be introduced to a remote sensing image in many ways, starting with the lens of the imaging hardware and ending at the digitization of the captured image. The reduction of noise without degradation of the remote sensing image has attracted much attention in the past. However, the traditional noise reduction methods can usually cause the degradation of the underlying image and cannot preserve the feature of structure in remote sensing image, especially two dimensional image brightness structures. With regard to the traditional speckle noise reduction methods, their results aren't very well even though the traditional methods are improved. In this paper, a method for speckle noise reduction of remote sensing image based on SUSAN is designed. This paper tests this method in a SPOT image of 128*128 suffering from speckle noise using 3 by 3 and 5 by 5 mask and gives results of quantitative and qualitative comparisons of the SUSAN noise filter with other traditional noise reduction methods. The results of the test prove that the SUSAN filter can effectively remove speckle noise and preserve edge and texture information. The processing speed of this algorithm is faster than that of the traditional noise reduction methods.

Dark frame subtraction using multiple dark frames

Abstract: The specification and drawings present a new method, apparatus and software product for a dark frame subtraction using multiple dark frames by storing only one frame at a time, i.e., using only one frame storage so that the amount of memory can be minimized. Divisional and multiplication algorithms can be used for the dark frame subtraction.

Color edge detection using the Skellam distribution as a sensor noise model

Abstract: Conventional edge detectors suffer from inherent image noise and threshold determination. In this paper, we propose a noble edge detector based on the noise distribution for CCD or CMOS cameras. By assuming the dominant photon noise, we model the distribution of intensity differences between two neighborhood pixels. Since it is well known that photon noise follows a Poisson distribution, we introduce a Skellam distribution, which is the difference of two Poisson random variables. We show experimentally that the Skellam distribution can be used to model the noise distribution of pixels that are captured from the same scene radiance. For estimating the noise distribution given a single pixel, we find the important property that the Skellam parameters are linearly related to the intensity value of pixels. This linearity enables us to determine noise parameters according to the intensity value. In addition, parameters of the line are preserved under illumination, scene and camera setting changes except for only a gain change. Based on the noise distributions, we calculate intensity allowances of three channels for each pixel given a confidence interval. We propose a noble edge detector by skipping a pre-processing step of conventional Gaussian smoothing which is the main obstacle for robust and accurate edge detection. If the difference of intensity exceeds the intensity allowance at least in a single channel, the in- between pixel is marked as an edge pixel. We demonstrate that without conventional Gaussian smoothing the noise-model based approach can automatically extract the fine details of image structures, such as edge and corners, independent of camera setting.

Fixed pattern noise correction for wide dynamic range linear-logarithmic pixels

Abstract: Pixels with the capability of linear response in low light regions and logarithmic response at high intensity have been proposed previously. A model for the complex response of these pixels has been devised that can be used to study the origins of fixed pattern noise and develop a fixed pattern noise correction procedure. The problem with correcting the fixed pattern noise in these pixels is the transition region between the linear and logarithmic operating regions. One way to avoid the problems from the transition region is to capture two outputs at different integration times within the same exposure. The resulting data can then be corrected for fixed pattern noise and converted to an equivalent linear or logarithmic response for subsequent image processing.