Showing papers on "Dark-frame subtraction published in 1998"

Adaptive-neighborhood filtering of images corrupted by signal-dependent noise.

Abstract: In many image-processing applications the noise that corrupts the images is signal dependent, the most widely encountered types being multiplicative, Poisson, film-grain, and speckle noise. Their common feature is that the power of the noise is related to the brightness of the corrupted pixel. This results in brighter areas appearing to be noisier than darker areas. We propose a new adaptive-neighborhood approach to filtering images corrupted by signal-dependent noise. Instead of using fixed-size, fixed-shape neighborhoods, statistics of the noise and the signal are computed within variable-size, variable-shape neighborhoods that are grown for every pixel to contain only pixels that belong to the same object. Results of adaptive-neighborhood filtering are compared with those given by two local-statistics-based filters (the refined Lee filter and the noise-updating repeated Wiener filter), both in terms of subjective and objective measures. The adaptive-neighborhood approach provides better noise suppression as indicated by lower mean-squared errors as well as better retention of edge sharpness than the other approaches considered.

Method and apparatus for processing digital images to suppress their noise and enhancing their sharpness

Abstract: A method of processing digital images to suppress their noise and enhance their sharpness. The method of suppressing noise and enhancing their sharpness includes steps of: (a) performing a sharpness enhancing process on original image data to generate sharpness enhance image data also performing a smoothing process on original image data to generate smoothed image data; (b) subtracting the smoothed image data from sharpness enhanced image data to extract a component consisting of both edges and noise; (c) performing nonlinear transformation on edge/noise containing component to separate a noise component and performing a subdividing process on the resulting noise component to produce a subdivided noise component; (d) separately detecting an edge component from original image data and determining weighting data for an edge region and a noise region from the detected edge component; (e) weighting edge/noise containing component with the weighting data for edge region to produce an edge enhancing component; and (f) subtracting subdivided noise component multiplied by a factor from sharpness enhanced image data while adding edge enhance component multiplied by another factor to sharpness enhanced image data so as to generate processed image data. The effect of the method is to process digital images to suppress their noise and enhance their sharpness.

Noise reduction by paired-microphones using spectral subtraction

Abstract: This paper proposes a method of noise reduction by paired microphones as a front-end processor for speech recognition systems. This method estimates noise using a subtractive microphone array and subtracts them from the noisy speech signal using spectral subtraction (SS). Since this method can estimate noise analytically and frame by frame, it is easy to estimate noise not depending on these acoustic properties. Therefore, this method can also reduce non-stationary noise, for example sudden noise when a door has just closed, which cannot be reduced by other SS methods. The results of computer simulations and experiments in a real environment show that this method can reduce LPC log spectral envelope distortions.

Dark frame subtraction

Abstract: A method of adjusting a portion of a dark frame in accordance with compensation values related to dark reference pixels of a picture frame to obtain an adjusted dark frame portion, and then subtracting the adjusted dark frame portion from a corresponding picture frame portion. The technique may be used to improve the accuracy of image sensors such as those used in digital cameras or video conferencing cameras by compensating for dark current noise. The technique may be applied to both CMOS image sensors and, in general, to any image sensors requiring dark frame subtraction. The techniques may also be used in conjunction with calibration of image sensors and imaging systems.

Estimation of frequency dependence and grey-level dependence of noise in an image

Abstract: Estimating spatial noise characteristics associated with an image acquired from an unknown digital image acquisition device is accomplished by a method, or system, which: provides predetermined default spatial noise characteristic information of the unknown digital image acquisition device; gathers user information related to the spatial noise characteristics of the unknown digital image acquisition device; gathers, from the acquired image, image data related to the spatial noise characteristics of the unknown digital image acquisition device; generates replacement data in response to the user information and the image data; and updates the predetermined default spatial noise characteristic information with the replacement data

Reducing noise in an imaging system

Abstract: A method includes generating a noise frame of data that is representative of a dark current image. Video frames of data are generated that represent video images. The video frames include noise. Information from the noise frame is used to compensate for the noise.

Structure preserving noise filtering of images using explicit local segmentation

Abstract: The trend in modern image noise filtering algorithms has been toward structure preservation by using only those neighbouring pixels which are similar to the current pixel in some way. In this paper we introduce a technique, call FUELS (filtering using explicit local segmentation), which explicitly segments the m /spl times/ m region encompassing the current pixel and filters using only those pixels from the same segment. By exploiting mask overlap an effective mask size of(2m-1)/spl times/(2m-1) is obtained, as well as robustness in regions which do not fit the image model. The algorithm can be iterated, and our results show FUELS to outperform existing algorithms both quantitatively and qualitatively.

Sharpening system adjusted for measured noise of photofinishing images

Abstract: A method of processing a digital image comprising the steps of: providing a pixellated digital image having noise components; measuring the noise components in the digital image with a noise estimation system to generate noise estimates; and sharpening the digital image with an image sharpening system which uses the noise estimates.

A new edge-detection based noise removal algorithm

Abstract: A method is disclosed for removing noise by distinguishing between edge and non-edge pixels, (120, 130, 140) and applying a first noise removal technique to pixels classified as non-edge pixels, (160) and a second noise removal technique for pixels classified as edge pixels, (150). The methodology operates on images while in a Color Filter Array (CFA) domain, prior to color interpolation and uses techniques suited to the classification, whether edge or non-edge.

Solid-state image sensing device with image pick-up bolometers for noise containing image signal and reference bolometers for estimating noise and read-out circuit for producing noise-free image signal

Abstract: A solid-state infrared image sensing device has an image pick-up pixel array having respective bolometers sequentially connected to a read-out circuit for producing an image carrying signal: However, the image carrying signal contains a fixed pattern noise due to the pattern of the bolometers: In order to produce a first reference signal representative of the standard magnitude of fixed pattern noise and a second reference signal representative of a deviation from the standard magnitude to the magnitude of fixed pattern noise at a selected image pick-up pixel, reference pixels are provided around the image pick-up pixel array.

An Algorithm To Extract Camera-shaking Degree And Noise Variance In The Peak-trace Domain.

Abstract: A method is proposed which estimates the degree of motion blur caused by the shaking of a digital camera during the exposure time. A new domain called the peak-trace domain is introduced to clearly specify the signal dominant region (used for extracting the camera-shaking degree) and the noise dominant region (used for estimating the noise variance). The experimental results show that the proposed method offers an efficient way to precisely detect the camera shaking and to restore the noisy blurred image.

Dark frame cancellation for cmos sensor-based tethered video peripherals

Abstract: Elimination of dark fixed pattern noise (DFPN) for tethered CMOS (36) sensor-based digital video camera (30) is supported by supplying and maintaining a host-based dark image cache (48). Since the camera is tethered to a host computer system (32) such as a PC, it takes advantage of the storage and processing capabilities of the host to manage the cache.

Relative effects of distortion and noise on target acquisition: the advisability of image restoration

Abstract: Any image acquired by optical, electro-optical, or electronic means is likely to be degraded by the environment. The resolution of the acquired image depends on the total modulation transfer function (MTF) of the system and the additive noise. Image restoration techniques can improve image resolution significantly; however, as the noise increases, improvements via image processing become more limited because im- age restoration increases the noise level in the image. We characterize the influence of the MTF and noise level on human target acquisition probability to ascertain the advantages, if any, of image restoration. Con- ditions when restoration would be advisable are determined. © 1998 So- ciety of Photo-Optical Instrumentation Engineers. (S0091-3286(98)01007-1)

Experimental and theoretical characterization of noise in a streak camera

Abstract: In an effort to improve the performances of streak cameras, experimental and theoretical studies were conducted, aimed at measuring the noise in a camera. This would be achieved through the evaluation of the camera's noise factor and by additionally pointing out the possible sources of noise. It was shown for our camera, a TSN 506 Thomson camera, that the noise factor is due partly to signal loss (factor 1,8) and partly to noise addition (factor 2,6). Moreover, noise was shown to be additive and Poissonian, mostly due to shot noise and photoelectric conversions.

Image enhancement using joint transform correlation

Abstract: An efficient technique for restoring the original from a blurred noise corrupted image using the concept of fringe- adjusted joint transform correlation is proposed. This technique employs Fourier plane apodization using the reference image power spectrum and noise power spectrum to suppress the effects of blur as well as input scene noise. The performance of the proposed technique has been enhanced significantly by employing the Fourier plane image subtraction especially for blurred noisy input scenes involving multiple identical objects. The image subtraction technique eliminates the unwanted zero-order term and crosscorrelation terms produced by similar input scene objects while alleviating the detrimental effects of blur and noise that may be present in the unknown input scene. Computer simulation results using blurred noise corrupted grey level input scenes are presented to verify the performance of the proposed technique.

Measuring noise in colour images

Abstract: Algorithms exist for removing unwanted noise from colour images; however little has been said about how to actually quantify the effect of these algorithms or generally how to quantify the amount of noise in a colour image. The standard methods include calculating the normalised mean square error (NMSE) between the original and the filtered image, calculating the mean chromaticity error (MCRE) between the original and the filtered image and of course visual inspection. The problem of MCRE and NMSE is that they only quantify the differences between two images-they are not a specific measure for noise in an image. Visual inspection is problematic because it is subjective. In this paper we propose a novel method for specifically calculating the amount of noise in a colour image. We give a general definition of how pixels could be classified as `noisy' based on well-established vector processing methodologies. We then describe how the sum of these `noisy' pixels can then be calculated and normalised to give a general measure of the percentage of neighbourhoods containing noisy pixels, which can then be used as a non-subjective estimate of the amount of noise in a colour image. (4 pages)

Preprocessing of images with signal-dependent noise in correlation

Abstract: In this work we analyze the effects of signal-dependent noise in the input scenes of optical correlators We propose several algorithms to process this noise and we evaluate their performance In particular we compare the strategy of restoring the input scene with noise and perform the correlation, with the alternative of correlating directly using filters designed to take into account the noise model in the input image