Showing papers on "Dark-frame subtraction published in 1999"

Adaptive error diffusion with improved edge and sharpness perception

Abstract: A method for adaptive error diffusion. The method includes the steps of receiving input image data, detecting edges in the input image data, and then adding noise to the input image data depending upon results of said edge detection. The amount of noise is higher for pixels with higher edge content, unless the pixel is on an edge that is already sharp. Once the noise is added the method performs error diffusion on the noise-enhanced image data and it produces output image data. Alternatively, noise can be added to the thresholding portion of the error diffusion process.

Method and apparatus for processing images

Abstract: According to the method and apparatus of processing a digital image for noise suppression and sharpness enhancement, sharpness enhancement and smoothing are performed on an original image to obtain a sharpness enhanced image and a smoothed image, from which image data containing subject image edges and noise (graininess) is determined; edge detection is performed from the original image to determine weighting data for a noise (or grainy) region, which is multiplied by the above image data to determined noise (grain) data of each color in a noise region, from which a black-and-white noise (grain) component and a color noise (dye grain) component are discriminated and separated based on a color correlation component obtained by calculating a color correlation or an obtained local grain coefficient representing a spatial magnitude and a variation magnitude of density variations due to graininess; the thus obtained black-and-white noise component and color noise component are multiplied by their suppressing coefficients to determine a black-and-white noise suppressing component and a color noise suppressing component, which are selectively removed from the sharpness enhanced image data, whereupon a processed image in which the noise suppression is achieved while retaining the sharpness enhancement in the edge region of the image is created. Consequently, graininess can be suppressed while enhancing image sharpness, without causing the unnatural artifacts which may be produced by the discoptinuousness of the boundary between a region from which graininess has been removed and a region where sharpness enhancement has been done.

How does real offset and gain correction affect the DQE in images from x-ray flat detectors?

Abstract: Offset and gain corrections are indispensable to exploit images from large image sensors, because of the pixel to pixel variation in dark current and sensitivity. However, an inappropriate correction may be detrimental to the signal to noise ratio of the raw image. This is especially critical in X-ray imaging, where the quantum noise is filtered by the detector spatial response. The noise power spectrum (NPS) in the corrected image is a combination of the initial noise spectrum in the raw image (quantum noise and electronic noise) with the noise in the offset and gain images. The dark image noise power just adds up to the noise power in the current image. The noise in the gain image alters the noise of the current image in a more intricate way. This is illustrated by simulations and experimental measurements. The conditions to safeguard the signal to noise ratio in the current image are detailed: There is an optimal number of dark and 'white' images to be averaged in order to keep their electronic and quantum noise negligible compared to that of the current image. Real conditions often force trade-offs between the desirable large number of offset/gain images to be averaged and the time effectively assigned to such acquisitions. Furthermore, the residual noise spectrum in the gain images is dependent on dose, uniformity of irradiation, temperature and detector spatial response. In the appropriate conditions, the intrinsic signal to noise ratio of an image can be preserved by offset and gain correction. Nevertheless, at high dose, the gain correction unavoidably introduces some high frequency proportional noise which degrades the DQE.

Image compression in signal-dependent noise.

Abstract: The performance of an image compression scheme is affected by the presence of noise, and the achievable compression may be reduced significantly. We investigated the effects of specific signal-dependent-noise (SDN) sources, such as film-grain and speckle noise, on image compression, using JPEG (Joint Photographic Experts Group) standard image compression. For the improvement of compression ratios noisy images are preprocessed for noise suppression before compression is applied. Two approaches are employed for noise suppression. In one approach an estimator designed specifically for the SDN model is used. In an alternate approach, the noise is first transformed into signal-independent noise (SIN) and then an estimator designed for SIN is employed. The performances of these two schemes are compared. The compression results achieved for noiseless, noisy, and restored images are also presented.

Restoration of lossy compressed noisy images

Abstract: Noise degrades the performance of any image compression algorithm. However, at very low bit rates, image coders effectively filter noise that may he present in the image, thus, enabling the coder to operate closer to the noise free case. Unfortunately, at these low bit rates the quality of the compressed image is reduced and very distinctive coding artifacts occur. This paper proposes a combined restoration of the compressed image from both the artifacts introduced by the coder along with the additive noise. The proposed approach is applied to images corrupted by data-dependent Poisson noise and to images corrupted by film-grain noise when compressed using a block transform-coder such as JPEG. This approach has proved to be effective in terms of visual quality and peak signal-to-noise ratio (PSNR) when tested on simulated and real images.

Digital image processing method and system including noise reduction and tone scale adjustments

Abstract: A method of processing a digital image, include the steps of: specifying a noise control parameter; employing the noise control parameter to process the digital image to reduce the noise in the digital image; and employing the noise control parameter to process the digital image to adjust the tone scale of the digital image.

Spatio-temporal filtering method for noise reduction during a pre-processing of picture sequences in video encoders

Abstract: A method of filtering noise from digital pictures includes selecting a first set of pixels including the union of a pixel of the current picture to be filtered and a second set of pixels temporally and spatially near the pixel. A certain number of extended sums of values assumed by as many pre-established weight functions of the intensity of a selected video component on the first set of pixels is also calculated. The second set of pixels may belong to the current picture or to a preceding picture. Several noise filters for digital pictures are also provided.

Image sensing apparatus employing dark image data to correct dark noise

Abstract: An apparatus has an image sensing device, an instruction device for instructing to execute a plurality of image sensing operations with different image sensing times of the image sensing device, and a signal processing device for performing a first image sensing operation for making the image sensing device perform an image sensing operation in an exposure state to obtain a sensed image signal, and a second image sensing operation for making the image sensing device perform an image sensing operation in a non-exposure state to obtain a sensed image signal, and processing the sensed image signal obtained by the first image sensing operation by the sensed image signal obtained by the second image sensing operation. The signal processing device changes the second image sensing operation in response to the instruction of the instruction device.

Reducing dark current noise in an imaging system

Abstract: Dark current noise may be compensated for in a digital imaging sensor by measuring the temperature of a silicon diode embedded on the same integrated circuit with the image sensor This information may be used together with initial dark current calibration information, to provide dark current compensation on the fly during image capture In some embodiments this may avoid the need for multiple shutter operations or repeatedly capturing a dark frame and then capturing a regular image frame

Clock control device used in image formation

Abstract: Video data (default data in case of a black original) output from a CCD line sensor 405 upon reading an image while a light source is kept OFF corresponds to beat noise contained in video data obtained upon reading an image while the light source is ON. After the beat noise data is stored, the correction data stored in a correction data storage unit is subtracted from video data read by a normal technique while the light source is ON in a correction memory, thus executing correction for removing beat noise. After the beat noise is removed in this way, when an image is formed under the control of a printer control unit, an image free from any beat noise can be obtained as an output image.

Improved dark frame subtraction

Abstract: A method of adjusting a portion of a dark frame (116) 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 sensor (617) 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 sensor 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.

Mixed-expectation image-reconstruction technique.

Abstract: An iterative method of reconstructing degraded images is developed from consideration of a mixed-noise imaging situation. Both photon noise in the image itself and postdetection Gaussian noise are combined by use of the standard maximum-likelihood method to produce a mixed-expectation reconstruction technique that demonstrates good performance in the presence of both noise sources. The new algorithm is evaluated through computer simulations.

Noise estimation method and apparatus for noise adaptive ultrasonic image processing

Abstract: A noise-adaptive method for processing an ultrasonic image set forms a filtered image set having a selectively enhanced noise component as compared to the original image set. A noise parameter is generated as a function of the image set and the filtered image set, and then the noise parameter is used in ultrasonic image processing. A background noise image is generated from the noise parameter and the original image and is used in ultrasonic image processing.

Method for reducing shadows and/or noise in a digital image

Abstract: Briefly, in accordance with one embodiment of the invention, a method of reducing shadows and/or noise in a digital image includes the following. A noise floor for the digital image is estimated. A threshold level for a difference image of the digital image and a background image is determined based, at least in part, on the noise floor estimate. The digital image is modified based, at least in part, on the determined threshold level and the difference image.

Method and apparatus for white noise reduction in video images

Abstract: A method and system for reducing white noise in images. The method does not require knowledge of the image blur or noise statistics, and can remove noise without causing excessive image blur. The image is separated into frequency bands which are then thresholded to remove small image changes, i.e. noise, while maintaining larger changes which are signals. The thresholded components are then recombined to produce an output image with reduced white noise.

Digital noise reduction through selective pixel comparison

Abstract: A method of reducing noise in a digitally sampled image is achieved in which an entire frame image is divided into regions which are used to determine the relative noise level of the static image over a period of several frames. The relative noise level is taken as a measurement and is then used by the firmware to make adjustments during the digital sampling process. By using less than the entire frame of the image information the method of reducing noise can be implemented without requiring the presence of a frame buffer.

Image processing device, image processing method, learning device and learning method

Abstract: Input image signals, to which noise is added on a transmission line or the like, are fed, and output image signals, from which the noise is eliminated by a classification adaptive processing, are outputted. In the classification adaptive processing, prediction coefficients are determined in advance for each class by learning to determine a class reflecting the noise components in the input image signals, and a predicted pixel value is generated by a linear combination between the prediction coefficients of the class and the values of a plurality of pixels contained in a plurality of frames including the pixel of concern in the input image signals. The predicted pixel value is free from influence of noise. The motion of the pixel of concern is detected, and motion the pixels used for determining the class and the pixels used for the prediction operations are subjected motion correct thereby reflecting the noise components accurately.

Noise Reduction in Digital Images

Abstract: html http://www.cis.rit.edu/research/thesis/bs/1999/jobes/Abstract.html#Abstract 1 of 1 10/11/2007 10:24 AM Noise Reduction in Digital Images

Implementation of noise reduction IC using a recursive temporal filter with motion detection

Abstract: We describe a practical implementation of a recursive noise reduction filter which employs an adaptive temporal filtering algorithm with motion detection and can minimize a variant of artifacts occurring in digital image sequences.

Image process in imaging through a scattering medium using fs electronic holography

Abstract: Aimed at imaging technology through scattering medium using fs electronic holography, a set of image process algorithm is put forward. This algorithm can be divided into three stages. First, every hologram is pre-processed, whose contrast is enhanced. Second, the first-order spatial spectrum is low-pass-filtered through a two-step process, so that high-frequency noise can be removed. Finally, many reconstructed images are ensemble-averaged. This stage can smooth random noise and is advantageous to restraining the speckle noise of image. The operation of this algorithm shows that all of processes in the three stages have obvious effects on improving image quality.