Showing papers on "Dark-frame subtraction published in 2005"

Imaging device and image processing program for estimating fixed pattern noise from partial noise output of available pixel area

Abstract: An imaging device of the present invention includes an image capturing unit, a noise obtaining unit, a fixed noise calculating unit, and a noise eliminating unit. The image capturing unit generates image data by photoelectrically converting, pixel by pixel, a subject image formed on an available pixel area of a light-receiving surface. The noise obtaining unit reads a noise output from a partial area of the available pixel area. The fixed noise calculating unit calculates an estimation of fixed pattern noise of the available pixel area based on the noise output read from the partial area. The noise eliminating unit subtracts the fixed pattern noise from the image data.

Imager row-wise noise correction

Abstract: An imager having optically and electrically black reference pixels in each row of the imager's pixel array. Since the reference pixels of each row experience the same row-wise noise as active imaging pixels in the associated row, the signals from the reference pixels are used to cancel out row-wise noise from the row of imaging pixels. The reference pixels are designed such that their photosensors are physically or effectively removed from the row-wise noise correction, thus rendering them electrically black or dark. As such, the reference pixels can be used to provide row-wise noise correction without the adverse effects of warm and hot pixels.

Tracking apparatus and methods using image processing noise reduction

Abstract: Improved apparatus and methodology for image processing and object tracking that, inter alia, reduces noise. In one embodiment, the methodology is applied to moving targets such as missiles in flight, and comprises processing sequences of images that have been corrupted by one or more noise sources (e.g., sensor noise, medium noise, and/or target reflection noise). In this embodiment, a multi-dimensional image is acquired for a first time step t; the acquired image is normalized and sampled, and then segmented into target and background pixel sets. Intensity statistics of the pixel sets are determined, and a prior probability image from a previous time step smoothed. The smoothed prior image is then shifted to produce an updated prior image, and a posterior probability image calculated using the updated prior probability. Finally, the position of the target is extracted using the posterior probability image. A tracking system and controller utilizing this methodology are also disclosed.

Mltistage bilateral noise filtering and edge detection for color image enhancement

Abstract: Removing noise while preserving and enhancing edges is one of the most fundamental operations of image/video processing. When taking pictures with digital cameras, it is frequently found that the color images are corrupted by miscellaneous noise and, hence, noise filtering is necessary. The difficulty is that usually the filtering will reduce the sharpness of the image. On the other hand, optical lens imperfections are usually equivalent to spatial low pass filters and tend to result in blurred images. It is customary to apply edge enhancement algorithm on the image in order to improve the sharpness, but this process usually increase the noise level as a by-product. In this paper, we present a new integrated approach to address these issues. The proposed approach combines color edge detection, bilateral noise filter, and edge enhancement based on suitable color spaces. The experimental results show that the proposed approach can effectively reduce the noise while preserving and enhancing edges.

Bayesian methods for noise reduction in image processing

Abstract: Improved methodology for image processing and object tracking that, inter alia, reduces noise. In one embodiment, the methodology is applied to moving targets, and comprises processing sequences of images that have been corrupted by one or more noise sources (e.g., sensor noise, medium noise, and/or target reflection noise). A likelihood or similar logical construct (e.g., Bayes' rule) is applied to the individual images (or aggregations thereof) of an image sequence in order to generate a posterior image for each observed image. The posterior images are fed-forward to the determination of the posterior image for one or more subsequent images (after smoothing), thereby making these subsequent determinations more accurate. The net result is a more accurate and noise-reduced representation (and location) of the target in each image.

Fast method for noise level estimation and integrated noise reduction

Abstract: This paper describes a fast method for noise level estimation and denoising. Specifically, we address the problem of estimating the standard deviation of additive white Gaussian noise in digital images; the computed value is used to reduce Gaussian noise and eliminate defective pixels in a raw digital image. The method is particularly suitable for implementation in low power mobile devices with imaging capabilities such as camera phones, as well as digital still cameras (DSC).

Image processing apparatus, electronic camera, scanner, and image processing method

Abstract: An image processing apparatus which calculates noise values based on signal levels of image signals and, reduces based on the noise values, the noise included in image signals which is output from a subject image sensor, including: a noise value output unit which, takes a certain image sensor as a baseline image sensor, stores correspondence relations between signal level values and noise values of output signals from the baseline image sensor, and outputs as first noise values the noise values of the baseline image sensor corresponding to signal level values of the image signals based on the correspondence relations; and, a noise value correction unit which compensates the first noise values to obtain second noise values corresponding to the subject image sensor using a prescribed variable which relates the noise characteristics of the baseline image sensor and of the subject image sensor.

Image processing based on local noise statistics

Abstract: Processing of a digital image includes applying a function to a plurality of pixels in the image, the function relating a noise statistic to pixel intensity values. By applying the noise statistic function, a noise statistic value is produced for each of the pixels. Each of the pixels is processed with respect to its noise statistic value.

Method and apparatus for removing hot pixels in a digital camera

Abstract: An apparatus and method of removing fixed pattern noise in a digital camera are provided. The apparatus includes a signal processing unit that calculates location information of the fixed pattern noise from a dark image photographed using an ISO and exposure time that are different from an ISO and exposure time used to photograph a general image, and removes the fixed pattern noise from the general image using the location information. The fixed pattern noise can be effectively removed while reducing the total photographing time in comparison to conventional apparatuses and methods.

Image feature and noise detection based on statistical hypothesis tests and their applications in noise reduction

Abstract: In many video processing applications in the field of consumer electronics such as digital TV, it is well understood that the presence of a noise limits the performance of video enhancement functions due to the time-varying characteristics of the noise. The basic difficulty is that the noise and the signal are difficult to be distinguished. This paper proposes image feature and noise detection algorithms, which effectively distinguish the noise from the image feature or vice versa. Specifically, the proposed algorithms provide a way of measuring the degree of noise with respect to the degree of image feature. The fundamental idea behind the proposed algorithms is to derive a statistical measure to estimate the fact that a noise has a random characteristic whereas an image feature has a spatial correlation among the associated neighbor samples. With the proposed algorithms, many video enhancement algorithms such as noise reduction or sharpness enhancement can be adaptively performed although a time varying noise is presented.

Image capture apparatus and image capture method

Abstract: For a V-line noise occurring in an image due to a defect in a VCCD of an image sensor, a position where a V-line noise is expected to occur when an image capture apparatus is in an initial state (initial noise position) is previously stored in a noise address memory. Then, a V-line noise is corrected by either using information about the initial noise position or detecting a position of the V-line noise, depending factors responsible for a temperature of the image sensor (a temperature of a substrate of the image sensor, a time period which has elapsed since the image capture apparatus was activated, and establishment or non-establishment of a continuous photographing mode), in other words, depending on a state of the image capture apparatus.

Method for reducing noise in images

Abstract: There is provided a method of reducing block noise, mosquito noise and other image noises in an image by a filtering process using a smoothing filter, which block noise, mosquito noise and other image noises being caused at the time of decoding encoded, compressed image data on a block-by-block basis. The method includes changing the extent or intensity of the image noise reduction in the filtering process in a continuous or stepwise manner according to an output size or expansion rate of an image to be outputted to printer paper, photographic paper or other output media, thereby allowing the extent or intensity of the image noise reduction to be increased as the output size or expansion rate of the image increases. This method is capable of allowing the extent of the image noise reduction applied to image data to be perceived in a similar fashion, irrespective of the output size.

Method and apparatus for removing noise from a digital image

Abstract: An apparatus and method of removing fixed pattern noise in a digital image processing apparatus are provided. The apparatus includes a controlling unit adjusting a number of image frames of a same image photographed by an imaging device and an exposure time of a shutter, and controlling a movement direction of the imaging device, which photographs the image; and a signal processing unit which median-combines the image frames of the same image photographed by the imaging device during the adjusted exposure time after moving the imaging device. The total photographing time is reduced by skipping a process of photographing a dark image and fixed pattern noise can be effectively removed by median-combining the image frames photographed while physically moving the imaging device between the photographing of consecutive image frames to remove the fixed pattern noise.

System and method for subtracting dark noise from an image using an estimated dark noise scale factor

Abstract: An image processing system subtracts dark noise out of images based on a dark noise scale factor. The image processing system includes an image sensor for capturing a current image and producing current image data representing the current image. The current image data includes both a dark noise signal and an image signal. The dark noise scale factor for the current image is estimated from the current image data and reference image data representing a reference dark noise signal. The reference dark noise signal is scaled by the dark noise scale factor to produce a scaled dark noise signal from which the current image data is subtracted to produce the image signal.

Digital image denoising

Abstract: Denoising of a plurality of pixels of a color digital image includes analyzing the plurality of pixels to identify those pixels having a high likelihood of containing significant noise; and forcing those pixels having a high likelihood of containing significant noise to be photometrically coherent with nearby pixels having a lower likelihood of containing significant noise.

Ultrasound imaging with reduced noise

Abstract: Signal processing techniques reduce the impact of noise (including speckle noise and shot noise) on ultrasound images by reducing the intensity of pixels that are probably noise and increasing the intensity of pixels that are probably signal. The decision of whether a given pixel is probably noise or probably signal is made based on spectral characteristics of the samples in and around the given pixel, based on knowledge of the expected spectral characteristics of the signal and the expected spectral characteristics of the noise.

Noise reduction device for reducing noise in image using blackout image, electronic camera, program, and method

Abstract: The noise reduction device includes an image storage unit, a blackout image processing unit, and a noise processing unit. The image storage unit captures image data obtained by imaging a field with an image sensor, and stores the image data therein. The blackout image processing unit captures blackout image data obtained by imaging by the image sensor that is shaded, and extracts a specific noise component of the blackout image data. The noise processing unit reduces a noise in the image data based on the specific noise component of the blackout image data.

Imaging device, noise elimination method and noise elimination program

Abstract: PROBLEM TO BE SOLVED: To provide an imaging device which is capable of accurately detecting a defect even if dark current noise occurs when performing noise reduction, and preventing as much as possible reduction of an imaging dynamic range caused by increase of dark current noise, and noise elimination method and noise elimination program using said imaging device. SOLUTION: The imaging device is provided with: an imaging signal acquisition means for acquiring an imaging signal resulting from exposure using an imaging device in a light non-shielding state; a dark state signal acquisition means for acquiring a dark state signal of the imaging device in a light shielding state; a threshold setting means for setting a threshold based on the acquired dark signal; a defect detection means for detecting a defect of an acquired dark state image based on the set threshold; a noise elimination means for eliminating noise by subtracting the dark state signal from the acquired imaging signal; and a defect correction means for correcting a signal from which noise is eliminated based on the detected defect. COPYRIGHT: (C)2006,JPO&NCIPI

Compressive Optical MONTAGE Photography Initiative: Noise and Error Analysis

Abstract: High resolution images are calculated from sub-Nyquist sampled data. The COMP-I program uses focal plane coding to set sub-bandlimited sampling. An analysis of this approach to noise and alignment errors is presented.

Motion and Noise Detection for Adaptive Spatio-Temporal Filtering of Medical X-Ray Image Sequences

Abstract: Spatio-temporalfilters are used to improve the perceived quality of X-ray image sequences exhibiting severe noise in real-time. The strength of spatial and temporal filtering has to be adapted locally in order to avoid artifacts. We propose a method processing the positive and negative pixel values of difference images independently in order to detect regions dominated by motion and single pixels dominated by noise. In the context of threshold determination for noise-adaptive binarization using Euler numbers, the influence of noise and motion on Euler curves is investigated.

Image processing device, image processing method, electronic camera, and scanner

Abstract: In order to make it possible to reduce the noise which is generated in an imaging device 1 in an accurate manner, after the dark current noise has been eliminated from the signal from the imaging device 1 by a black level compensation section 2, noise reduction processing is performed by a noise reduction section 3 and elimination of shot noise is performed, and subsequently white balance compensation is performed by a white balance compensation section 4. When noise reduction processing is performed with this type of structure, it becomes possible to reduce the noise in an accurate manner, since the noise reduction is performed after the black level compensation and moreover before the white balance compensation.

Low noise sample and hold circuit

Abstract: An image sensor includes a plurality of pixels for capturing incident light that is converted to a signal representing an image; wherein noise is combined with a signal representing both the image and a reset level; a plurality of dark reference pixels that generate noise that substantially correspond or equally correspond to the noise in the image and reset level; and a sample and hold circuit that reads out the image signal and cancels or substantially cancels out the noise from the image signal and reset level by canceling the noise from image and reset level with the noise generated from the dark reference pixels.

Noise-robust hands-free speech recognition based on spatial subtraction array and known noise superimposition

Abstract: We propose a spatial subtraction array (SSA) and known noise superimposition to achieve a noise-robust hands-free speech recognition which can be used in human-robot interaction. In the proposed SSA, noise reduction is achieved by subtracting the estimated noise power spectrum from the target speech power spectrum to be enhanced in the mel-scale filter bank domain. This offers a realization of error-robust spatial spectral subtraction with few computational complexities. In addition, we introduce known noise superimposition technique in the mel-scale filter bank domain, and utilize the matched acoustic model for the known noise. This can compensate the acoustic model mismatch and mask the residual noise component in SSA. The experimental results obtained under a real environment reveal that word accuracy of the proposed method is greater than that of the conventional method even when the target user moves between -10 and +10 degrees around the microphone array.

Method for reducing mosquito noise

Abstract: A method of reducing mosquito noise in a digital image. As mosquito noise is often most plainly visible in the “background” of an image (e.g., the sky or some other backdrop to objects within an image), the luminance value of the background of the image is first determined. Then, the luminances of the pixels of the image are compared against this “background luminance” to determine which should be considered as part of this background. The luminances of these background pixels are then averaged so as to smooth out the representation of the background, and reduce mosquito noise.

Noise robust speech recognition based on spatial subtraction array

Abstract: Summary form only given. We propose a spatial subtraction array (SSA) and known noise superimposition to achieve a robust hands-free speech recognition under noisy environments. In the proposed SSA, noise reduction is achieved by subtracting the estimated noise power spectrum from the target speech power spectrum to be enhanced in the mel-scale filter bank domain. This offers a realization of error-robust spatial spectral subtraction with few computational complexities. In addition, we introduce known noise superimposition techniques in the mel-scale filter bank domain, and utilize the matched acoustic model for the known noise. This can compensate the acoustic model mismatch and mask the residual noise component in SSA. The experimental results obtained under a real environment reveal that word accuracy of the proposed method is greater than that of the conventional method even when the target user moves between -10 and +10 degrees around the microphone array.

Imaging System And Image Processing Program

Abstract: Acquired video signals are forwarded to pre-processing block ( 103 ) for sampling, gain amplification and A/D conversion, and then forwarded to image buffer ( 104 ). Video signals in image buffer ( 104 ) are forwarded to noise estimation block ( 106 ). On the basis of taking conditions, video signals and a reference noise model, noise estimation block ( 106 ) works out the quantity of noise per ISO sensitivity, and per color signal. The calculated noise quantity is forwarded to noise reduction block ( 105 ). On the basis of the noise quantity estimated at the noise estimation block ( 106 ), the noise reduction block ( 105 ) applies noise reduction processing to video signals in image buffer ( 104 ), and video signals after noise reduction processing are forwarded to signal processing block ( 108 ).

Sub-Poissonian shot noise

Abstract: Shot noise, the time-dependent fluctuations in electrical current caused by the discreteness of the electron charge, is well known to occur in solid-state devices, such as tunnel junctions, Schottky barrier diodes and p-n junctions. Most textbooks on electronic devices will tell you that there is no shot noise in metallic resistors, just thermal noise and a frequency-dependent noise known as 1/f noise. However, our basic knowledge of electrical conduction in small devices has advanced to the stage where it is clear that this notion does not hold.

Differential image sensor with high common mode rejection

Abstract: Image processing applications like tracking of moving objects typically involve the subtraction of two successive images. Doing this without the need for a large digital memory requires a sensor chip that does the subtraction in the analog domain. This paper provides insight in the critical issues of the design of such a differential image sensor. The presented sensor has two operation modes: a very low noise correlated double sampling (CDS) mode and a differential image (DI) mode. This sensor is optimized to have a high common mode rejection ratio (CMRR) which results in a small influence from the content of the image onto the differential image.