Showing papers on "Dark-frame subtraction published in 2010"

Image De-noising by Various Filters for Different Noise

Abstract: Image processing is basically the use of computer algorithms to perform image processing on digital images. Digital image processing is a part of digital signal processing. Digital image processing has many significant advantages over analog image processing. Image processing allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing of images. Wavelet transforms have become a very powerful tool for de-noising an image. One of the most popular methods is wiener filter. In this work four types of noise (Gaussian noise , Salt & Pepper noise, Speckle noise and Poisson noise) is used and image de-noising performed for different noise by Mean filter, Median filter and Wiener filter . Further results have been compared for all noises.

Impulse Noise Removal in Digital Images

Abstract: This paper introduces the concept of image fusion of filtered noisy images for impulse noise reduction. Image fusion is the process of combining two or more images into a single image while retaining the important features of each image. Multiple image fusion is an important technique used in military, remote sensing and medical applications. Five different filtering algorithms are used individually for filtering the image captured from the sensor. The filtered images are fused to obtain a high quality image compared to individually denoised images. In-order to better appraise the noise cancellation behavior of our fusion technique from the point of view of human perception, an edge detection is performed using canny filter for the fused image. Experimental results show that this method is capable of producing better results compared to individually denoised images.

Noise removal in compound image using median filter

Abstract: Compound image is a combination of text, picture and graphs. Noise reduction in compound image is necessary to maintain the quality of images. Noise is added into an image at the time of image acquisition (or) image capturing. After capturing, image preprocessing is necessarily done to correct and adjust the image for further classification and segmentation. From the literature study different filtering techniques are available to reduce the noise from compound images. Normally the filters are used to improve the image quality, suppress the noise. This paper proposes median filtering technique for removing salt & pepper noise from various types of compound images. Several examples were conducted to evaluate the performance of the median filter on noise.

A 2.7e- temporal noise 99.7% shutter efficiency 92dB dynamic range CMOS image sensor with dual global shutter pixels

Abstract: A low-noise global shutter CMOS image sensor is a next challenge to expand the market for CMOS image sensors. A low-noise global electronic shutter can be used for various applications such as high-speed imaging, machine vision and mechanical shutterless digital still cameras. A commonly used five transistor (5T) global shutter pixel using a floating diffusion memory suffers from large temporal noise due to kTC noise (reset noise) and large dark current [1]. Two-stage charge transfer pixels such as a seven transistor (7T) active pixel [2] and a dual pinned-diode active pixel presented by the authors [3] cancel the kTC noise. However, such structures have an issue of low shutter efficiency due to leakage from a photodiode to storage gate or diode. Furthermore, the 7T pixel suffers from dark current and transfer noise because of the use of surface-channel storage gates. The dual pinned-diode pixel of the previous design has problem of non-linearity due to poor charge transfer efficiency from a photodiode to storage diode.

Simultaneous measurement of noise and spatial resolution in PET phantom images

Abstract: As an aid to evaluating image reconstruction and correction algorithms in positron emission tomography, a phantom procedure has been developed that simultaneously measures image noise and spatial resolution. A commercially available 68Ge cylinder phantom (20 cm diameter) was positioned in the center of the field-of-view and two identical emission scans were sequentially performed. Image noise was measured by determining the difference between corresponding pixels in the two images and by calculating the standard deviation of these difference data. Spatial resolution was analyzed using a Fourier technique to measure the extent of the blurring at the edge of the phantom images. This paper addresses the noise aspects of the technique as the spatial resolution measurement has been described elsewhere. The noise measurement was validated by comparison with data obtained from multiple replicate images over a range of noise levels. In addition, we illustrate how simultaneous measurement of noise and resolution can be used to evaluate two different corrections for random coincidence events: delayed event subtraction and singles-based randoms correction. For a fixed number of iterations of the maximum-likelihood expectation-maximization algorithm, the singles-based correction gave rise to higher noise than delayed event subtraction. However, when noise and resolution were measured simultaneously it was shown that singles-based randoms correction gave rise to lower noise than delayed event subtraction for a fixed spatial resolution. The proposed method of simultaneously measuring image noise and spatial resolution is useful for evaluating reconstruction algorithms and may aid standardization of data collection between centers.

Block noise detection and filtering

Abstract: Systems and methods for block noise detection and filtering are disclosed. One embodiment includes, computing difference magnitudes in pixel values for adjacent pixels in the image. The difference magnitudes can include horizontal difference magnitudes for horizontally adjacent pixels and vertical difference magnitudes for vertically adjacent pixels. One embodiment further includes using normalized sums of the difference magnitudes to determine a set of noise characteristics of the block noise and a set of image characteristics of the image and configuring inputs to the block noise filter using the set of noise and image characteristics.

Image processing equipment and digital camera

Abstract: In order to perform a pipeline processing on a noise filtering processing, which uses a multi-resolution noise filtering, with a few line memories, the way to select an area of a reduced image which is used by the extraction of the low-frequency noise component from the reduced image is modified. By extracting the low-frequency noise component from the area being selected by the modified way, it is possible to suppress a volume of image data which needs to be read prior to the noise filtering object pixel.

Automatically detecting and classifying noises in document images

Abstract: Image filtering to remove noise in document images follows two different approaches. The first one uses human classification of the noise present in an image for identifying a noise filter to use. The second approach is to blindly apply a batch of filters to an image. The former approach, although widely used, may insert noise in the filtering process due to the incorrect classification of the noise or even unsuitable filtering parameters. This paper presents a new paradigm for document image filtering. It aims at doing a more accurate and computationally efficient document cleanup by pre-characterizing the noise that is present in the document based on a set of human labeled training samples. The current focus of the project is on pre-characterization of the following types of noise: back-to-front interference or bleed through, skew and orientation, blur and framing.

Quaternion-based color image filtering for impulsive noise suppression

Abstract: It is difficult to precisely detect all impulsive noise in color images due to the nonstationarity caused by edges and fine details. For many pixels, we can not absolutely classify them as noisy or noise-free, but can only describe them using the likelihood that they are corrupted by impulsive noise. Based on this consideration, we present a new filtering solution to removing impulsive noise from color images. The proposed method first utilizes the unit transforms of quaternions to represent the chromaticity difference of two color pixels, and then divides the image into noise-free and possible noisy pixels. Finally it performs adaptive weighted vector median filtering operations on only the possible noisy pixels to suppress noise. The new weighting mechanism is based on a joint spatial/quaternion-chromaticity criterion, which ensures that pixels with different contamination likelihoods have different contributions to the filter's output. The extensive simulation results indicate that the proposed method significantly outperforms some other well-known multichannel filtering techniques.

Signal-dependent raw image denoising using sensor noise characterization via multiple acquisitions

Abstract: Accurate noise level estimation is essential to assure good performance of noise reduction filters. Noise contaminating raw images is typically modeled as additive white and Gaussian distributed (AWGN); however raw images are affected by a mixture of noise sources that overlap according to a signal dependent noise model. Hence, the assumption of constant noise level through all the dynamic range represents a simplification that does not allow precise sensor noise characterization and filtering; consequently, local noise standard deviation depends on signal levels measured at each location of the CFA (Color Filter Array) image. This work proposes a method for determining the noise curves that map each CFA signal intensity to its corresponding noise level, without the need of a controlled test environment and specific test patterns. The process consists in analyzing sets of heterogeneous raw CFA images, allowing noise characterization of any image sensor. In addition we show how the estimated noise level curves can be exploited to filter a CFA image, using an adaptive signal dependent Gaussian filter.

A detail preserving filter for impulse noise removal

Abstract: It is known that digital images are frequently corrupted by impulse noise due to errors generated in camera sensors, analog-to-digital conversion and communication channels. Therefore, it is imperative to remove impulse noise in order to facilitate the subsequent processing such as edge detection, segmentation, analysis, and pattern recognition etc. Many linear and nonlinear filtering techniques have been proposed earlier to remove impulse noise, however these filter often bring along blurred and distorted image of details. Filtering an image to attenuate noise while keeping the image details preserved is one of the most important issues. In this paper a detail preserving filter for impulse noise removal is proposed, which is based on the soft-switching median (SWM) filter. In the first, the impulse noise candidates are detected by arranging the pixels in order in the sliding window. In the second, we analyze the noise candidates and classify them into noise-free pixels, noisy pixels and detail pixels (edges and smooth changing regions). Finally, the process employed the rank-ordered mean filter (ROM) to remove the corrupted pixels and the details can be restored and preserved. Extensive experiments indicate that the proposed method can effectively restore images corrupted with impulse noise and performs significantly better in terms of noise suppression and detail preservation than a number of existing nonlinear techniques.

A novel technique for removal of high density impulse noise from digital images

Abstract: Mostly researchers use all pixels within a window to filter out the impulse noise. They increase the size of neighboring pixels with the increase of noise density. However, this estimate of all neighboring pixels does not give promsing results for high level of noise density. In contrast, in the paper, we propose impulse noise removal scheme that emphasizes on few noise-free pixels. The proposed iterative algorithm search the noise-free pixels within a small neighborhood. The noisy-pixel is then repalced with the average estimated from noise-free pixels. The iterative process continues until all noisy-pixels of the corrupted image are filtered. The performance of the proposed method is tested using various impulse noise corrupted images. The simulation results show the proposed scheme is capable of removing high density of impulse noise effectively while preserving the fine image details.

Noise reduction system, image pickup system and computer readable storage medium

Abstract: A noise reduction system for performing noise reduction processing for an image signal taken in from an image pickup system, includes a local area extracting unit which sequentially extracts, from the image signal, a local area including a target pixel for which the noise reduction processing is performed; a first noise reducing unit which performs random noise reduction processing for the local area; a second noise reducing unit which performs impulsive noise reduction processing for the local area; and a combining unit which combines an image signal which has been subjected to the noise reduction processing by the first noise reducing unit and an image signal which has been subjected to the noise reduction processing by the second noise reducing unit.

Methods of modeling an integrated noise in an image sensor and methods of reducing noise using the same

Abstract: A method is for processing a Bayer domain signal of an image sensor to model an integrated noise in the image sensor. The method includes receiving the Bayer domain signal of the image signal, setting a plurality of noise models using the Bayer domain signal, and determining an integrated noise level in the image sensor based on the plurality of noise models. The noise models include a dark-current noise model, a shot noise model and a fixed-pattern noise model.

Noise reduction in fluoroscopic image sequences for joint kinematics analysis

Abstract: Analysis of dynamic videofluoroscopic can provide spine kinematic data with an acceptable low X-ray dose. Estimation of the kinematics relies on accurate recognition of vertebrae positions and rotations on each radiological frame. In previous works we presented a procedure for automatic tracking of vertebra motion by smoothed gradient operators and template matching in fluoroscopic image sequences. A limitation to the accurate estimation of the kinematics by automatic tracking of vertebrae motion, independently by the specific methodology employed (e.g. manual marking, corner or edge automatic detection, etc.), is mainly due to noise: low-dose X-ray image sequences exhibit severe signal-dependent noise that should be reduced, while preserving anatomical edges and structures. Noise in low-dose X-ray images originates from various sources, however quantum noise is by far the more dominant noise in low-dose X-ray images and other sources can be neglected. Signal degraded by quantum noise is commonly modeled by a Poisson distribution, but it is possible to approximate it as additive zero-mean Gaussian noise with signal-dependent variance. In this work we propose a digital spatial filter for reducing noise in low-dose X-ray images. The proposed filter is based on averaging of only similar pixels (whose grey level is contained within ±3σ) instead of spatial averaging of all neighbouring pixels. The effectiveness of the filter performance was evaluated by fluoroscopic image sequence processing, comparing the results of the automatic vertebra tracking on filtered and unfiltered images.

Methods and apparatus for noise reduction in digital images

Abstract: Methods and apparatus for reducing or removing noise in digital images. Image noise reduction methods are described that may be applied to grayscale and color images, for example RGB images. An image noise reduction method may, before applying a noise filtering technique, transform the image values from linear space to flat noise space in which the noise is independent of the signal. An edge-preserving noise filtering technique may then be applied to the image in flat noise space. After noise filtering is applied, the image is transformed from flat noise space back to linear space. For color images, the flat noise space may be converted from linear color space to luminance-chrominance space before applying the noise filtering technique so that different filters can be applied to luminance and color channels. After applying the noise filtering technique, the image is converted back to linear color space.

Moving image noise reduction processing apparatus, computer-readable recording medium on which moving image noise reduction processing program is recorded, and moving image noise reduction processing method

Abstract: There is provided a moving image noise reduction processing apparatus for reducing noise in a digitalized time-series image signal, including: a spatial noise reduction unit applying spatial noise reduction processing to the image signal; a previous buffer unit storing an image signal to which at least the spatial noise reduction processing has been applied; and a temporal noise reduction unit performing temporal noise reduction processing based on an image signal to be processed to which the spatial noise reduction processing has been applied in the spatial noise reduction unit and an image signal which is stored in the previous buffer unit and is earlier than the image signal to be processed.

Digital picture noise reduction by combining high-noise and low-noise processed pictures

Abstract: A method for processing a digital picture is disclosed. The method may include steps (A) to (C). Step (A) may generate a first picture by processing the digital picture using a first noise reduction technique in a circuit. Step (B) may generate a second picture by processing the digital picture using a second noise reduction technique. The first noise reduction technique may achieve a higher noise reduction than the second noise reduction technique. Step (C) may generate an output picture by combining the first picture and the second picture.

Chroma noise reduction in DCT domain using soft-thresholding

Abstract: The chroma noise effect seriously reduces the quality of digital images and videos, especially if they are acquired in low-light conditions. This paper describes the DCT-CNR (Discrete Cosine Transform-Chroma Noise Reduction), an efficient chroma noise reduction algorithm based on soft-thresholding. It reduces the contribution of the DCT coefficients having highest probability to be corrupted by noise and preserves the ones corresponding to the details of the image. Experiments show that the proposed method achieves good results with low computational and hardware resources requirements.

Camera noise reduction for machine vision systems

Abstract: A method for reducing noise in an image from an image capture device includes filter the image using both an offset fixed pattern noise filter and a gain fixed pattern noise filter. Thereafter, the image is filtered using a remnant fixed pattern noise filter to reduce remnant fixed pattern noise.

Preserving noise during editing of a signal

Abstract: Techniques for introducing background noise segments into signal data are provided. The background noise segments are constructed from a background noise print extracted from the signal data. The background noise print may be user specified, or automatically identified by the signal editing tool. The background noise print may be stored with, and subsequently loaded as part of, the project associated with a signal. The background noise segments that are generated based on the background noise print may have different durations than the background noise print itself.

Apparatus, method and computer-readable medium removing noise of color image

Abstract: Provided is an apparatus, method and computer-readable medium removing noise of an image. The apparatus may include a channel image correction unit to correct remaining channel images excluding a currently processed channel image, from among a plurality of channel images, using the currently processed channel image, a noise removal unit to remove noise of the currently processed channel image, using the corrected remaining channel images and the currently processed channel image, and a color image reconstruction unit to reconstruct a color image in which noise is removed, by combining the plurality of channel images in which noise is removed when the noise of the plurality of channel images is removed.

Robust Statistics based Filter to Remove Salt and Pepper Noise in Digital Images

Abstract: Visual information transmitted in the form of digital images is becoming a major method of communication in the modern age, but the image obtained after transmission is often corrupted with noise. The received image needs processing before it can be used in applications. This paper represents a robust statistics based filter to remove salt and pepper noise in digital images. The function of the algorithm is to detect the corrupted pixels first since the impulse noise only affect certain pixels in the image and the remaining pixels are uncorrupted. The corrupted pixels are replaced by an estimated value using the proposed robust statistics based filter. The proposed method perform well in removing low to medium density impulse noise with detail preservation upto a noise density of 70% compared to standard median filter, weighted median filter, recursive

A novel approach for image denoising using dynamic tracking with new threshold technique

Abstract: During the image acquisition and communication the image is corrupted by noise. This is a classical problem in the field of signal or image processing. In this paper local statistical function is used to find the percentage of noise affected in the noise image. Dynamic denoising algorithm is used simultaneously as a tool for enhancing and tracking image pixel. The pixels are chosen based on the prediction of noise pixels in dynamic tracking. Neighshrink and sure shrink threshold is applied next as a new combination threshold to reduce a noise image in wavelet domain. The PSNR results demonstrated in this paper indicate the many advantages of the proposed dynamic technique using overstate combine Neighshrink and Sureshrink (Neighsureshrink) method.

Image sensing apparatus and control method therefor

Abstract: An image sensing apparatus comprises an image sensor configured to convert an optical image of a subject into image signals by photoelectric conversion. The image sensing apparatus acquires information for estimating a magnitude of a dark current in the image sensor, and, on the basis of the acquired information, selects any one of vertical linear noise correction processing, black subtraction processing, and normal readout processing in which neither the vertical linear noise correction processing or the black subtraction processing is carried out. Then, the image sensing apparatus carries out the vertical linear noise correction processing to correct a vertical linear noise in an image if the vertical linear noise correction processing is selected, or the black subtraction processing to correct a vertical linear noise and a fixed pattern noise in an image if the black subtraction processing is selected.

Image processing method to reduce compression noise and apparatus using the same

Abstract: An image processing method which adaptively reduces compression noise of a digital image and an image processing apparatus using the same are provided. The image processing method determines weight to reduce compression noise of an image based on local information and frame information, and processes the image according to the weight. Therefore, noise is adaptively reduced according to an image, and also noise is reduced with the optimal extent.

Suppression for light source intensity noise in high-precision FOG

Abstract: The light source intensity noise in the superfluorescent erbium-doped fiber source is a major factor that affects the angle random walk(ARW) of FOG.To reduce the ARW and improve the precision of FOG,a method for reducing the light source intensity noise is put forward,i.e.the digital subtraction in a field programmable gate array(FPGA),which is based on the correlation between the two parts of the coupler.The output data of FOG was analyzed by Allan variance without the digital subtraction and with the digital subtraction respectively.The results indicate that 33% reduction is demonstrated in the ARW of FOG.This method is simple to implement,high reliable,and easy to maintain.

Noise removal from binarized text images

Abstract: Noise is common in binarized images which are the result of extracting text from the embedded text in an image. It degrades the performance of character recognition module. In this paper, a robust algorithm is proposed to eliminate noise from the binarized text image based on the text-stroke width information. First, salt-pepper-like noises are eliminated by a morphological filter, which is to enhance the correctness of estimating the text-stroke width. Finally, a method based on the text-stroke width information is proposed to extract text from noise images. Experiments on a wide variety of binarized images with salt-pepper noise, and cluttered noise reveal the feasibility and effectiveness of our proposed approach in removing the noise.

A new focus measure for fusion of multi-focus noisy images

Abstract: Images usually are corrupted by noise produced by image sensors in many practical applications. Image noise may cause miscalculation of image definition because the traditional focus measures can't give a correct evaluation of image clarity in the presence of noise, which introduces significant errors in the results of image fusion. A new measure based on lifting wavelet transform was proposed for the existence of image noise by the energy of high-frequency and low-frequency subbands after the second-level decomposition. Experimental results show that the new measure presented in this paper can provide better performance than other focus measures for noisy images.