Showing papers on "Dark-frame subtraction published in 2003"

A method for modifying the image quality parameters of digital radiographic images.

Abstract: A new computer simulation approach is presented that is capable of modeling several varieties of digital radiographic systems by their image quality characteristics. In this approach, the resolution and noise characteristics of ideal supersampled input images are modified according to input modulation transfer functions (MTFs) and noise power spectra (NPS). The modification process is separated into two routines-one for modification of the resolution and another for modification of the noise characteristics of the input image. The resolution modification routine blurs the input image by applying a frequency filter described by the input MTF. The resulting blurred image is then reduced to its final size to account for the sampling process of the digital system. The noise modification routine creates colored noise by filtering the frequency components of a white noise spectrum according to the input noise power. This noise is then applied to the image by a moving region of interest to account for variations in noise due to differences in attenuation. In order to evaluate the efficacy of the modification routines, additional routines were developed to assess the resolution and noise of digital images. The MTFs measured from the output images of the resolution modification routine were within 3% of the input MTF The NPS measured from the output images of the noise modification routine were within 2% of the input NPS. The findings indicate that the developed modification routines provide a good means of simulating the resolution and noise characteristics of digital radiographic systems for optimization or processing purposes.

Image quality evaluation based on image weighted separating block peak signal to noise ratio

Abstract: Traditional objective image quality evaluation measures, such as the MSE or the PSNR, only represent the total difference between the original images and reconstructed images. However in some case, such as there are few large error pixels and many small error pixels in an image, they have not a consistent a result with subjective measure. To cope with this drawback, we propose a new image quality evaluation measure that is called geometry weighted separating block peak signal to noise ratio (GWSB/spl I.bar/PSNR). It corresponds to human visual observations very well. The experimental result shows that this measure is valid, reliable, wieldy and extensible.

A two-sensor noise reduction system: applications for hands-free car kit

Abstract: This paper presents a two-microphone speech enhancer designed to remove noise in hands-free car kits. The algorithm, based on the magnitude squared coherence, uses speech correlation and noise decorrelation to separate speech from noise. The remaining correlated noise is reduced using cross-spectral subtraction. Particular attention is focused on the estimation of the different spectral densities (noise and noisy signals power spectral densities) which are critical for the quality of the algorithm. We also propose a continuous noise estimation, avoiding the need of vocal activity detector. Results on recorded signals are provided, showing the superiority of the two-sensor approach to single microphone techniques.

Communication system with adaptive noise suppression

Abstract: A signal-to-noise ratio dependent adaptive spectral subtraction process eliminates noise from noise-corrupted speech signals. The process first pre-emphasizes the frequency components of the input sound signal which contain the consonant information in human speech. Next, a signal-to-noise ratio is determined and a spectral subtraction proportion adjusted appropriately. After spectral subtraction, low amplitude signals can be squelched. A single microphone is used to obtain both the noise-corrupted speech and the average noise estimate. This is done by determining if the frame of data being sampled is a voiced or unvoiced frame. During unvoiced frames an estimate of the noise is obtained. A running average of the noise is used to approximate the expected value of the noise. Spectral subtraction may be performed on a composite noise-corrupted signal, or upon individual sub-bands of the noise-corrupted signal. Pre-averaging of the input signal's magnitude spectrum over multiple time frames may be performed to reduce musical noise.

A noise reduction filter for full-frame data imaging devices

Abstract: This paper describes a method for video sequences denoising that exploits extra-information provided by the image sensor. Fixed pattern noise and temporal noise are removed by analyzing a series of lines placed at the top of the imager.

Noise reduction in color digital images using pyramid decomposition

Abstract: A method of removing noise from a color digital image, including receiving an original color digital image including a plurality of pixels represented in a primary-color space; producing at least one residual digital image and at least one base digital image from the original color digital image, the base digital image having a lower spatial resolution then the original color digital image; producing a noise reduced base digital image by removing noise from the residual image and the base digital image with a noise reduction filter and combining the noise reduced base digital image with the noise reduced residual image to produce a reconstructed digital image having reduced noise; transforming the reconstructed digital image into a luminance-chrominance color space; repeating the above process to produce a reconstructed luminance-chrominance digital image; and transforming the reconstructed luminance-chrominance digital image into a noise reduced digital image in the original primary color space.

Fixed pattern noise subtraction in a digital image sensor

Abstract: A fixed pattern noise subtraction method in a digital imaging system incorporating a digital image sensor includes: acquiring a reference image of the digital image sensor when the digital image sensor receives no illumination, storing a reference value of an operating parameter associated with the reference image where the reference image is indicative of the fixed pattern noise associated with the digital image sensor, storing a model describing the behavior of the fixed pattern noise as a function of the operating parameter. Then, the method continues with acquiring a first image, measuring a current value of the operating parameter associated with the first image, calculating a noise prediction image by extrapolation of the reference image in accordance with the model and based on the current value and the reference value of the operating parameter, and subtracting the noise prediction image from the first image to generate a final image.

Easily testable image operators: the class of circuits where evolution beats engineers

Abstract: The paper deals with a class of image filters in which the evolutionary approach consistently produces excellent and innovative results. Furthermore, a method is proposed that leads to the automatic design of easily testable circuits. In particular we evolved "salt and pepper" noise filters, random shot noise filters, Gaussian noise filters, uniform random noise filters, and edge detectors.

Low noise image sensing system and method for use with sensors with very small pixel size

Abstract: The image sensing system includes integrating charge in an image sensor array; transferring the charge out of the image sensor array; converting the charge to a digital signal; combining the digital signal with digital data stored in a memory device to form an integrated signal; storing the integrated signal in the memory device such that the integrated signal becomes the digital data; and repeating the above steps multiple times during a frame time cycle. This system allows for very small pixel sizes in the image sensor. The digital integration process eliminates the need for using correlated double sampling circuits to reduce kTC noise, and is also beneficial for reduction of analog-to-digital digitization noise.

Peer region determination based impulsive noise detection

Abstract: A peer region determination (PRD) algorithm for impulsive noise detection in digital images is proposed; it removes random-valued impulsive noise while preserving very fine image details. This algorithm determines the peer region for each pixel adaptively by finding the variation of pixel values in a 5/spl times/5 filter window. If the number of member pixels in the peer region is very small, the pixel being processed is thought to be isolated from other pixels and thus considered as impulsive noise. In addition, this noise detector can be easily modified to perform feature selective filtering. Experimental results show that the proposed noise detection algorithm outperforms other existing non-linear filters and adaptive noise detection based filters in noise removal and image detail preservation. Finally, the concept of the PRD algorithm applied to other image processing applications is discussed.

Method for random point and patterned noise reduction in digital images

Abstract: A technique is provided for filtering noise in digital image data, particularly random point or spike noise. Image data may be rank order filtered and absolute differences between ordered values computed to create a mask. Blending is performed based upon a likelihood that individual pixels are or exhibit spike noise. The rank order filtered values may be used directly for blending, or the original image may be shrunk and then expanded to provide a rapid and computationally efficient spike noise reduction alternative.

Imaging device and imaging method for use in such device

Abstract: An imaging device which assures high-quality image information free from a line noise by extracting line noise information from a taken image, detecting an existence of an extrinsic line noise in the image, and correcting the line noise if it exists. The imaging device comprises a memory circuit for storing image pick-up outputs at imaging of a two-dimensional area sensor comprising photosensors or radiation sensors, an extrinsic line noise detection unit for detecting an extrinsic line noise in the image pick-up outputs stored in the memory circuit, and an arithmetic processing circuit for calculating an output quantity of the detected extrinsic line noise. The line noise is eliminated by correcting the image pick-up outputs on the basis of the obtained output quantity of the extrinsic line noise.

Scene-based correction of image sensor deficiencies

Abstract: This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences. Fixed pattern noise is the dominantnoise component for many infrared detector systems ...

Image de-blurring and application to SPOT5 THR satellite imaging

Abstract: We propose a restoration fixed chosen noise restoration (FCNR) taking into account together the noise instrument and the DCT compression noise of SPOT5 THR images. The coloration and the non-stationarity of the noise is managed by working in the wavelet packet coefficient space. The restoration is a lossless information process. The dimension of the SPOT5 THR images is 24000 /spl times/ 24000 and the complexity of the calculation of FCNR is 700 operations per pixel.

A temporal noise reduction filter based on image sensor full-frame data

Abstract: This paper describes a temporal filter aimed at the simultaneous cancellation of fixed pattern noise and temporal noise from image sequences by exploiting all the data provided by a typical image sensor (e.g. CCD/CMOS).

Holographic recording and reproducing system

Abstract: PROBLEM TO BE SOLVED: To remove a noise component superposed on a reproduced image by a simple system configuration. SOLUTION: An image detected by a CCD image sensor 108 when only a non-modulated signal light is applied to a holographic memory 120 in which data is not recorded is ensured as a noise pattern. When data is reproduced, the previously obtained noise pattern is subtracted from a reproduced image detected by the CCD image sensor 108. COPYRIGHT: (C)2005,JPO&NCIPI

Identification of image noise sources in digital scanner evaluation

Abstract: For digital image acquisition systems, analysis of image noise often focuses on random sources, such as those associated with quantum signal detection and signal-independent fluctuations Other important noise sources result in pixel-to-pixel sensitivity variations that introduce repeatable patterns into the image data In addition, since most analyses use a nominally uniform target area to estimate image noise statistics, target noise can often masquerade as noise introduced by the device under test We described a method for distilling various fixed-pattern and temporal noise sources The method uses several replicate digital images, acquired in register In some cases, however, evaluation of digital scanners reveals, scan-to-scan variation in the image registration to the input test target To overcome this limitation, a modified noise estimation method is described This includes a step to correct this scan-to-scan misregistration We also show how the measurement of temporal and fixed pattern noise sources can be achieved via the noise color covariance from a single test image

Image sensor that uses a temperature sensor to compensate for dark current

Abstract: An image sensor that has a temperature sensor. The temperature sensor senses the temperature of a pixel array of the image sensor. The sensed temperature is used to scale a dark frame image generated by the pixel array. The scaled dark frame image is subtracted from a light image frame generated by the pixel array. The scaled dark image frame compensates for temperature variations in the pixel array. The scaled dark image frame may be generated by multiplying the dark frame by a scale factor(s). The scale factor may be computed from an equation or determined from a look-up table. The equation or look-up table may compensate for thermal gradients across the pixel.

Image processing apparatus, noise elimination method, and noise elimination program

Abstract: PROBLEM TO BE SOLVED: To provide an image processing apparatus, a noise elimination method, and a noise elimination program capable of properly eliminating noise in response to the motion of an image. SOLUTION: The image processing apparatus 100(1) includes: an image synchronization section 13 for synchronizing a frame image from an image input section 11 with a frame image from a frame storage section 12; a two-dimensional noise elimination section 14 for carrying out the elimination of the noise of the frame image by a two-dimensional noise elimination method; a motion detection section 15 for detecting the motion of the frame image; a frame circulation coefficient decision section 16 for deciding a frame circulation coefficient; a three-dimensional noise elimination section 17 for carrying out the elimination of the noise of the frame image by a three-dimensional noise elimination method; a composite ratio decision section 18 for deciding a composite ratio between a two-dimensional image eliminated image and a three-dimensional noise eliminated image; and a two-dimensional/three-dimensional composite section 19 for composing the two-dimensional image eliminated noise with the three-dimensional noise eliminated image. COPYRIGHT: (C)2005,JPO&NCIPI

Image processing method and a digital camera

Abstract: Effectively reducing a noise on the face region to improve the picture quality of the entire image A digital camera according to the invention includes: an digital signal processor for performing image processing including contour correction on a shot image; a face identifying section for analyzing an image after contour correction to generate face region information to identify the face region; a noise reducing section for performing noise reduction on the face region of the image after contour correction based on the face region information; a controller 106 for determining the photographing mode of the shot image and operating the face identifying section and the noise reducing section depending on the photographing mode.

An intelligent hardware structure for impulse noise suppression

Abstract: In this paper an intelligent hardware module suitable for the computation of an adaptive median filter (AMF) is presented. The proposed digital hardware structure is pipelined and parallel processing is used to minimize computational time. It is capable of processing gray-scale images of 8-bit resolution with 3/spl times/3 or 5/spl times/5-pixel image neighborhoods as options for the computation of the filter output. However, the system can be easily expanded to accommodate windows of larger sizes. The function of the proposed circuitry is to detect the existence of impulse noise in an image neighborhood and apply the median filter operator only when necessary. Moreover, the noise detection procedure can be customized so that a range of pixel values is considered as impulse noise. In this way, the integrity of edge and detail information of the image under process is preserved and blurring is avoided. The proposed digital structure was implemented in FPGA and it can be used in industrial imaging applications, where fast processing is of the utmost importance. As an example, the time required to perform filtering of a grayscale image of 260/spl times/244 pixels is approximately 7.6 msec. The typical system clock frequency is 65 MHz.

Imaging apparatus and imaging systems, and imaging method

Abstract: PROBLEM TO BE SOLVED: To provide an imaging apparatus for extracting line noise information from the images photographed, detecting the line noise in the images, and, if the noise is included, capable of obtaining a high quality image information with its line noise reduced by compensating the line noise. SOLUTION: The imaging apparatus comprises a memory circuit 110 for storing a photographed output when taking photographs by a two-dimentional area sensor 104 having a photosensor or a radiation sensor, a line noise detecting means 111 for detecting the line noise in photographed output stored in the memory circuit 110, and a processing circuit 112 for processing computing output value of the detected line noise. The compensation in the photographed output on the basis of the output value of the line noise obtained removes the line noise. COPYRIGHT: (C)2004,JPO

Noise of LCD display systems

Abstract: This paper presents the results of initial physical and psycho-physical evaluations of the noise of high resolution LCDs. Five LCDs were involved, having four different pixel structures. Spatial as well as temporal noise was physically measured with the aid of a high-performance CCD camera. Human contrast sensitivity in the presence of spatial noise was determined psycho-physically using square-wave patterns stimuli as well as square stimuli. The results demonstrate that spatial noise is the dominant noise in all LCDs, while temporal noise is insignificant and plays only a minor part. The magnitude of spatial noise of LCDs is in the range between that of CRTs with a P104 and that of CRTs with a P45. Of particular importance with respect to LCD noise is the contribution of the pixel structure to the Noise Power Spectrum, which shows up as sharp spikes at spatial frequencies beyond the LCDs' Nyquist frequency. Much more work is necessary to understand the impact of spatial noise on the diagnosis of malignant abnormalities like micro-calcifications.

Similarity based impulsive noise removal in color images

Abstract: In this paper a novel approach to the problem of impulsive noise reduction in color images based on the nonparametric density estimation is presented. The basic idea behind the new image filtering technique is the maximization of the similarities between pixels in a predefined filtering window. The new method is faster than the standard vector median filter (VMF) and preserves better edges and fine image details. Simulation results show that the proposed method outperforms standard algorithms of the reduction of impulsive noise in color images.

Two-Dimensional Noise Images

Abstract: Noise in an image can be defined as the unwanted part of that image. The noise may be random in some way, as is the pepper-and-salt appearance on a television screen when the station goes off the air, or it may be systematic, as with the ghost seen when an echo of the wanted signal arrives with a time delay after reflection from a hill. When the television image responds independently to sparks in a faulty thermostat in the nearby refrigerator or to a faulty ignition system on a passing motorcycle, the noise exhibits both random and systematic features. In other cases, the wanted signal may be random; thermal microwave or infrared radiation used for mapping the ground is of this nature. As a result, one person’s noise may be another person’s signal and vice versa. Very often it does not matter much what the character of the noise is, only its magnitude is needed, an attitude that is reflected in the term signal-to-noise ratio. As the examples show, the noise in an image need not be independent of, but can be closely connected with, the wanted signal itself. In the latter case if the signal is removed, the noise will change. When the noise is independent, it may be studied on its own in the absence of any wanted signal.

Image pickup system with noise estimator

Abstract: An image pickup system has a noise estimating unit which estimates the amount of noise contained in a digitized signal from an image pickup element composed of an array of a plurality of pixels, either for each pixel or for each specified unit area made up of a plurality of pixels, and a shooting conditions estimation unit which estimates the shooting condition when an image based on the signal is acquired. The amount of noise estimated by the noise estimating unit is corrected on the basis of the shooting conditions estimated by the shooting conditions estimation unit, and the noise in the signal is reduced on the basis of the corrected amount of noise.

Method for suppressing ghosting in X-ray pictures applies a subtraction process to correct an X-ray shot by processing residual image portions

Abstract: Multiple X-ray shots (7) for one or more objects are taken by impinging X-rays in a time sequence by connecting a solid-state detector in series in order to generate a visible image. A correcting image (10) corrects an X-ray shot by applying a subtraction process and a gain adjustment (13) through a gain image (12). An acquired image (14) is then subjected to further image-processing (15). An Independent claim is also included for an X-ray device for generating X-ray pictures with a source of X-rays, a solid-state detector and an image-processing device for correcting an X-ray picture taken.

Apparatus and method for reducing picture noise

Abstract: PURPOSE: An apparatus and a method for reducing picture noise are provided to easily calculate a motion adaptive constant of 1D noise reduction filter. CONSTITUTION: An apparatus for reducing picture noise includes a motion adaptive constant calculator(101) for calculating a motion adaptive constant in order to reduce noise of an input image adaptively to a degree of a motion of the input image. The motion adaptive constant calculator includes a noise level calculating unit(1011), a low pass filter(1012), a motion estimator(1013), and a motion adaptive constant calculating unit(1014). The noise level calculating unit calculates a noise level of the input image. The low pass filter low-pass-filers the input image in response to the noise level. The motion estimator estimates a degree of motion of the input image from the low-pass-filtered image and an image obtained by reducing noise of an image prior to the input image. The motion adaptive constant calculating unit calculates the motion adaptive constant based on the estimated degree of motion.

Method for reducing image noise

Abstract: A method for reducing image noise is provided. The procedure of the method is provided in the following steps. First, the color level scale of a scanned image of a document is reduced by a plurality of bits in order to subtract a noise level from the scanned image. Then the color level scales of all pixels of the image are recombined by a halftone pattern method in order to recover the color level scales. Finally the missing codes of the image are filled out by bit enhance method. Because of the color level scales of the proceeding image are not reduced, the scanned image quality does not be blurred by the method. Because the method does not minimize the color level of the proceeding image, the image noise can be reduced without blurring the scanned image and the quality of the image can be increased after the process thereof. And because each color level of the pixel in the image is subtracted by a noise level, some of the bits are removed and the capacity of the image file is decreased.

Improvement of the Noise Filtering and Image Registration Methods for PSP Experiments

Abstract: Image processing procedure for noise reduction and image registration in the PSP experiments is investigated. A few kinds of filter are examined for the shot noise reduction caused by the CCD camera. The algorithm to detect a marker cell located on the model surface is proposed and an appropriate number size is shown. The algorithm using the wavelet transform is investigated to sharpen the edge around the model. The results indicate that the method developed in this paper effectively removes the shot noise and corrects the displacement of the model during wind-on.Copyright © 2003 by ASME