Showing papers on "Dark-frame subtraction published in 1997"

SUSAN—A New Approach to Low Level Image Processing

Abstract: This paper describes a new approach to low level image processing; in particular, edge and corner detection and structure preserving noise reduction. Non-linear filtering is used to define which parts of the image are closely related to each individual pixel; each pixel has associated with it a local image region which is of similar brightness to that pixel. The new feature detectors are based on the minimization of this local image region, and the noise reduction method uses this region as the smoothing neighbourhood. The resulting methods are accurate, noise resistant and fast. Details of the new feature detectors and of the new noise reduction method are described, along with test results.

Edge-detection based noise removal algorithm

Abstract: What is disclosed is a method for removing noise by distinguishing between edge and non-edge pixels and applying a first noise removal technique to pixels classified as non-edge pixels and a second noise removal technique to pixels classified as edge pixels. 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.

Multi-channel speech enhancement in a car environment using Wiener filtering and spectral subtraction

Abstract: This paper presents a multichannel-algorithm for speech enhancement for hands-free telephone systems in cars. This new algorithm takes advantage of the special noise characteristics in fast driving cars. The incoherence of the noise allows to use adaptive Wiener filtering in the frequencies above a theoretically determined frequency. Below this frequency a smoothed spectral subtraction (SSS) is used to get an improved noise suppression. The algorithm yields better results in noise reduction with significantly less distortions and artificial noise than spectral subtraction or Wiener filtering alone.

Black pattern correction for charge transfer sensor

Abstract: An electronic imaging device employs black pattern correction for dark current in a charge transfer image sensor. The sensor is composed of image pixels having a characteristic black pattern of dark current in which the amplitude of the dark current for each pixel is dependent upon exposure time. A reference dark frame exposure is captured from the image sensor in the absence of light and dark frame pixel values are obtained. An exposure section regulates the exposure time of image light upon the image sensor and provides a corresponding plurality of image frame exposures; the image sensor thus generates a corresponding plurality of image frames each comprised of image frame pixel values. A processor then generates a correction factor from the dark frame pixel values and applies the correction factor to the image frame pixel values for the plurality of image frames to obtain corrected image frame pixel values that are modified for the black pattern. As a result, performance efficiency is increased by using a single reference dark frame exposure in the correction of many image frame exposures.

Electronic endoscope system for reducing random noise of a video signal

Abstract: An electronic endoscope system which captures an image of an object and displays the same on a display device is provided with a noise reduction system. The noise reduction system reduces noise included in a frame of an image signal for each color component. The noise-reduced color components are stored in a buffer, and then output as a video signal to be transmitted to a display where a color image of the object is displayed.

Method and apparatus for dark frame cancellation for CMOS sensor-based tethered video peripherals

Abstract: Elimination of dark fixed pattern noise (DFPN) for tethered CMOS sensor-based digital video cameras is supported by supplying and maintaining a host-based dark image cache. Since the camera is tethered to a host computer system such as a PC, it takes advantage of the storage and processing capabilities of the host to manage the cache. By using a dark image cache for updating of the currently applicable dark image for DFPN cancellation processing, operation of the camera shutter for acquiring dark images is dramatically reduced, thereby using less system resources such as power, and increasing the MTBF of the electromechanical devices such as the camera shutter and associated controls. Dark images are obtained at different integration, gain, and temperature operating characteristics of the camera and stored in the cache. The cached dark images are referenced on the host according to a fixed, predetermined dark column of data in video frames generated by the CMOS sensor image array of the camera. The dark column data represents a portion of the CMOS sensor image array which is permanently and totally shadowed for use during DFPN cancellation processing.

Noise performance of a color CMOS photogate image sensor

Abstract: We report on the noise performance of a color CMOS photogate image sensor that supports two levels of correlated double sampling and has high conversion gain at each pixel. Imager performance is limited by low quantum efficiency and dark current non-uniformity and not by read-out circuit temporal or fixed-pattern noise.

Reduction of nonstationary noise in telescope imagery using a support constraint.

Abstract: We demonstrate the use of image support constraints in a noise-reduction algorithm. Previous work has revealed serious limits to the use of support if image noise is wide-sense stationary in the frequency domain; we use simulation and numerical calculations to show these limits are removed for nonstationary noise generated by inverse-filtering adaptive optics image spectra. To quantify the noise reduction, we plot fractional noise removed by the proposed algorithm over a range of support sizes. We repeat this calculation for other noise sources with varying degrees of stationarity.

Method for correcting dark current of video camera device and video camera device using the method

Abstract: PROBLEM TO BE SOLVED: To reduce a dark current noise occurring at the time of long-term storage with high precision even at the time of temp. changing environment or camera gain change by adding the noises for the portion of several screens and averaging them so as to detect a dark current. SOLUTION: When the dark current noise stored in a video memory 13 is detected, the output of A/D 6 is added to a black level which is detected in an optical black cramp 3 by an adder 11 and, after that, inputted to a noise removing circuit 12 for removing a random noise, etc., having no correlative relation to a preceding image so as to be inputted to the video memory 13. The output of the video memory 13 is fed-back to the noise removing circuit 12 so that a noise removal adder 12 adds the noises for the portion of several screens and averages them. By the configuration, the noise removal adder 12 detects the dark current noise and the random noise for the portion of several screens and averages them so as to execute an operation for removing the random noise, etc., without correlative relation. COPYRIGHT: (C)1999,JPO

Digital subtraction angiograph

Abstract: PROBLEM TO BE SOLVED: To reduce quantum noises of X-rays and improve the guiding property of a catheter by suppressing the specific picture element value of a subtraction image and the positive or negative display gradation. SOLUTION: An X-ray high-voltage device 1 applies a high voltage to an X-ray tube display via the control signal from a CPU circuit 13 and irradiates X-rays to a subject 3. An optical image is formed by an image intensifier 4, it is photographed by a TV camera 5, and it is stored in a frame memory 11 as it is without being processed by an arithmetic unit 8. Gradation conversion and rewriting of the content of a lookup table 12 are made by the instruction of the CPU 13. The quantum noise of X-rays is normally distributed centering on 0 by subtraction, and the output is suppressed to 0 against the input in the fixed width centering on 0 to reduce the quantum noise. When the width for suppressing the output to 0 is changed and adjusted while the image is observed by the instruction of the CPU 13, the noise can be reduced.

Automatic estimation and removal of emission image noise

Abstract: The authors have developed a method of automatically estimating noise in 2and 3D emission images from the information intrinsic to them and then using this estimate to selectively remove the noise from the image. Unlike low cutoff filters, which reduce resolution and contrast, the morphing operation removes noise without adversely affecting these qualities. The noise estimation method uses contiguous volume analysis to obtain information about all of the hot spots in the image. If the image contains hot regions too small to contain information, the method analyzes their sizes and heights. This information is used to find a conservative estimate of noise for the image. The information about noise is used to perform an image morphing using a noise-specific structure. Erosion and dilation are morphing operations which remove image detail smaller than the specified structure, without distortion of the larger features. When applied to PET and SPECT images using the correct structure, these operations remove only noise, thereby improving image uniformity without degrading resolution or contrast.

Optimal interpolation for medical images using MAP method

Abstract: The problems of high resolution image reconstruction are approached in this project as an optimization problem. Assuming an ideal image is blurred, noise corrupted, and sub-sampled to produce the measured image, we pose the estimation of the enlarged image as a maximum-a-posteriori (MAP) restoration process and the mean field annealing optimization technique is used to solve the multi-model objective function. The iterative interpolation process incorporates two terms into its objective function. The first term is the 'noise' term which models the burring and subsampling of the acquisition system. By using the system point spread function and the noise characteristics, the measured pixels at the sub-sampled-grid are mapped into the grid of the original image. A second term, the a-priori term is formulated to fore the prior constraints such as noise smoothing and edge preserving into the interpolation process. The resulted image is a noise reduced, deblurred, and enlarged image. The proposed algorithm are used to zoom several medical images, along with existing techniques such as pixel replication, linear interpolation, and spectrum extrapolation. The resulted images indicate that the proposed algorithm can smooth noise extensively while keeping the image features. The images zoomed by other methods suffer from noise and look less favorable in comparison.© (1997) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Speech Recognition in Noisy Environments using the NOise Spectrum Estimation based on the Histogram Technique

Abstract: Spectral subtraction is widely-used preprocessing technique for speech recognition in additive noise environments, but it requires a good estimate of the noise power spectrum. In this paper, we employ the histogram technique for the estimation of noise spectrum. This technique has advantages over other noise estimation methods in that it does not requires speech/non-speech detection and can estimate slowly-varying noise spectra. According to the speaker-independent isolated word recognition in both colored Gaussian and car noise environments under various SNR conditions. Histogram-technique-based spectral subtraction method yields superier performance to the one with conventional noise estimation method using the spectral average of initial frames during non-speech period.