Showing papers on "Dark-frame subtraction published in 2013"

A brief study of various noise model and filtering techniques

Abstract: In image processing, noise reduction and restoration of image is expected to improve the qualitative inspection of an image and the performance criteria of quantitative image analysis techniques Digital image is inclined to a variety of noise which affects the quality of image. The main purpose of de-noising the image is to restore the detail of original image as much as possible. The criteria of the noise removal problem depends on the noise type by which the image is corrupting .In the field of reducing the image noise several type of linear and non linear filtering techniques have been proposed . Different approaches for reduction of noise and image enhancement have been considered, each of which has their own limitation and advantages.

Random resampling masks: a non-Bayesian one-shot strategy for noise reduction in digital holography

Abstract: Holographic imaging may become severely degraded by a mixture of speckle and incoherent additive noise. Bayesian approaches reduce the incoherent noise, but prior information is needed on the noise statistics. With no prior knowledge, one-shot reduction of noise is a highly desirable goal, as the recording process is simplified and made faster. Indeed, neither multiple acquisitions nor a complex setup are needed. So far, this result has been achieved at the cost of a deterministic resolution loss. Here we propose a fast non-Bayesian denoising method that avoids this trade-off by means of a numerical synthesis of a moving diffuser. In this way, only one single hologram is required as multiple uncorrelated reconstructions are provided by random complementary resampling masks. Experiments show a significant incoherent noise reduction, close to the theoretical improvement bound, resulting in image-contrast improvement. At the same time, we preserve the resolution of the unprocessed image.

Survey on various noises and techniques for denoising the color image

Abstract: Images are often degraded by noises. Noise can occur during image capture, transmission, etc. Noise removal is an important task in Image processing. In general the results of the noise removal have a strong influence on the quality of the image processing technique. Several techniques for noise removal are well established in color image processing. The nature of the noise removal problem depends on the type of the noise corrupting the im age. In the field of image noise reduction several linear and non linear filtering methods have been proposed. Denoising of image is very important and inverse problem of image processing which is useful in the areas of image mining, image segmentation, pattern recognition and an important preprocessing technique to remove the noise from the naturally corrupted image by the different types of noises. The wavelet techniques are very effective to remove the noise also use of its capability to confine the power of a signal in little convert of energy values. This Paper reviews on various noises like Salt and Pepper noise, Gaussian noise etc and various techniques available for denoising the color image.

Fast restoration of natural images corrupted by high-density impulse noise

Abstract: In this paper, we suggest a general model for the fixed-valued impulse noise and propose a two-stage method for high density noise suppression while preserving the image details. In the first stage, we apply an iterative impulse detector, exploiting the image entropy, to identify the corrupted pixels and then employ an Adaptive Iterative Mean filter to restore them. The filter is adaptive in terms of the number of iterations, which is different for each noisy pixel, according to the Euclidean distance from the nearest uncorrupted pixel. Experimental results show that the proposed filter is fast and outperforms the best existing techniques in both objective and subjective performance measures.

A new method to remove random-valued impulse noise in images

Abstract: This paper presents a new method for detecting random-valued impulse noise (RVIN) in images. The proposed method is based on similar valued neighbor criterion and the detection of the noisy pixels are realized in maximum four phases. After the corrupted pixels detected in each phase, the median filtering is performed for only these pixels. As such, corrupted pixels are suppressed gradually at the end of the each phase. The performance of the proposed method is evaluated on different test images and compared with ten different comparison filters from the literature. It is shown from simulation results that proposed method provides a significant improvement over comparison filters.

Towards an Evaluation of Denoising Algorithms with Respect to Realistic Camera Noise

Abstract: The development and tuning of denoising algorithms is usually based on readily processed test images that are artificially degraded with additive white Gaussian noise (AWGN). While AWGN allows us to easily generate test data in a repeatable manner, it does not reflect the noise characteristics in a real digital camera. Realistic camera noise is signal-dependent and spatially correlated due to the demosaicking step required to obtain full-color images. Hence, the noise characteristic is fundamentally different from AWGN. Using such unrealistic data to test, optimize and compare denoising algorithms may lead to incorrect parameter tuning or sub optimal choices in research on denoising algorithms. In this paper, we therefore propose an approach to evaluate denoising algorithms with respect to realistic camera noise: we describe a new camera noise model that includes the full processing chain of a single sensor camera. We determine the visual quality of noisy and denoised test sequences using a subjective test with 18 participants. We show that the noise characteristics have a significant effect on visual quality. Quality metrics, which are required to compare denoising results, are applied, and we evaluate the performance of 10 full-reference metrics and one no-reference metric with our realistic test data. We conclude that a more realistic noise model should be used in future research to improve the quality estimation of digital images and videos and to improve the research on denoising algorithms.

Robust Digital Camera Identification Based on Pairwise Magnitude Relations of Clustered Sensor Pattern Noise

Abstract: Owing to the rapid progress in digital camera technologies, a large amount of image content is distributed on the World Wide Web. Digital camera identification, which is the identification of the source camera of an input image, is becoming increasingly important for presenting evidence in a court and helping police investigations. In recent years, a digital camera identification method using the image sensor's pattern noise has received considerable attention. Photo-response non-uniformity (PRNU) noise is mainly generated by the existence of differences between the sensitivities of pixels, and it is useful as a fingerprint of a camera. However, the PRNU noise of an image is usually contaminated by random noise and scene content and affected by the image processing engine, which inhibits stable identification. In this paper, we propose a novel digital camera identification method using the pairwise magnitude relations of image sensor noise, which are robust to noise contamination. By performing experiments, we demonstrate that the proposed method can identify the source cameras of query images with high accuracy.

Image noise removal with detail preservation for random valued impulse noise

Abstract: Edge information is the most vital high frequency information of an image, filtering an image to reduce noise while keeping the image details preserved is one of the most important issues. In this paper we proposed a method in which double median filtering stage is used to preserve edge details, which enhance the quality of image. The algorithm is carried out in two stages; first stage is detection stage which detects noisy pixels, second stage is a filtering stage in which median is calculated twice. The goal of proposed algorithm is to remove high density noise in digital images while keeping edge details. The proposed method gives better image quality with high peak signal to noise ratio (PSNR) and reduced mean square error (MSE). Results of proposed algorithm have been analyzed in terms of visual and quantitative results.

Applying perceptually correct 3D film noise

Abstract: Perceptually correct noises simulating a variety of noise patterns or textures may be applied to stereo image pairs each of which comprises a left eye (LE) image and a right eye (RE) image that represent a 3D image. LE and RE images may or may not be noise removed. Depth information of pixels in the LE and RE images may be computed from, or received with, the LE and RE images. Desired noise patterns are modulated onto the 3D image or scene so that the desired noise patterns are perceived to be part of 3D objects or image details, taking into account where the 3D objects or image details are on a z-axis perpendicular to an image rendering screen on which the LE and RE images are rendered.

Method of noise reduction in digital X-rayograms

Abstract: The invention relates to the field of digital image processing and can find use in suppression of noise in digital images, formed by high-energy radiation, including X-ray radiation. Specifically, the invention relates to a method for suppression of noise in digital x-ray images. The objective of the invention is to provide a method for noise suppression in digital X-ray images that has extended functionality, specifically, a method that makes it possible to reduce residual noise level and amount of artifacts in the form of discontinuities, directed along local orientation of object borders in textured image areas, to reduce residual LF noise level, and to eliminate over-smoothing (excessive filtering) of fine details. The technical innovation achieved is the improvement of digital image processing quality.

A Review on Speckle Noise Filtering Techniques for SAR Images

Abstract: the synthetic aperture radar (SAR) preferred for many applications like reconnaissance, surveillance, and targeting because of high resolution and capability of all weather day and night observations. Besides the advantages it suffers from a major problem caused by random phase fluctuations of the return electromagnetic signals. These fluctuations appear as noise in the processed image and known as spackle noise. Speckle noise in SAR is generally more serious, causing difficulties for image interpretation hence it is required to be filtered, but because of multiplicative nature it requires different treatment then additive noise. This paper presents a review and analysis on different spatial and transform domain methods already proposed for filtering Speckle noise in SAR.

Forensic sensor pattern noise extraction from large image data set

Abstract: The sensor pattern noise (SPN) can be regarded as the unique identity of a digital camera which is highly useful in digital image forensics [1, 2]. Existing methods [1, 2] which works by denoising each individual natural image often took an investigator a long time and great efforts to collect sufficient photos of diversified enough natural scenes. These processes are hard to repeat or standardized for officially using by an authority. In this work, we create noise image data set by taking photos of random noises displayed on a high definition monitor and propose a homomorphic based SPN extraction method. It offers the forensic researcher a fast way to create a large image data set in a few minutes. And the extraction method only needs to denoise once, which is highly efficient to deal with large numbers of photos. We compared the source camera identification performance of the proposed SPN extraction method to a prior state-of-art with identical experimental settings. The experimental results confirm the effectiveness of the proposed method.

Joint Noise Level Estimation from Personal Photo Collections

Abstract: Personal photo albums are heavily biased towards faces of people, but most state-of-the-art algorithms for image denoising and noise estimation do not exploit facial information. We propose a novel technique for jointly estimating noise levels of all face images in a photo collection. Photos in a personal album are likely to contain several faces of the same people. While some of these photos would be clean and high quality, others may be corrupted by noise. Our key idea is to estimate noise levels by comparing multiple images of the same content that differ predominantly in their noise content. Specifically, we compare geometrically and photo metrically aligned face images of the same person. Our estimation algorithm is based on a probabilistic formulation that seeks to maximize the joint probability of estimated noise levels across all images. We propose an approximate solution that decomposes this joint maximization into a two-stage optimization. The first stage determines the relative noise between pairs of images by pooling estimates from corresponding patch pairs in a probabilistic fashion. The second stage then jointly optimizes for all absolute noise parameters by conditioning them upon relative noise levels, which allows for a pair wise factorization of the probability distribution. We evaluate our noise estimation method using quantitative experiments to measure accuracy on synthetic data. Additionally, we employ the estimated noise levels for automatic denoising using "BM3D", and evaluate the quality of denoising on real-world photos through a user study.

An Adaptive Enhancement of X-Ray Images

Abstract: Most of the X-ray images are no truly isotropic and its quality varies depending on penetration of X-rays in different anatomical structures and on the technologies of their obtaining. The noise problem arises from the fundamentally statistical nature of photon production. This paper presents an approach for X-ray image enhancement based on contrast limited adaptive histogram equalization (CLAHE), following by morphological processing and noise reduction, based on the Wavelet Packet Decomposition and adaptive threshold of wavelet coefficients in the high frequency sub-bands of the shrinkage decomposition. Implementation results are given to demonstrate the visual quality and to analyze some objective estimation parameters in the perspective of clinical diagnosis.

High dynamic range image acquisition using flash image

Abstract: We propose a denoising technique using multiple exposure image integration. When acquiring a dark scene, the detail of the dark area is often deteriorated by sensor noise. For a high dynamic range image acquisition, denoising in dark areas is a critical issue, since the dark area is, in general, enhanced by a tone-mapping and the noise is made more visible when displaying it on an output devise. In our method, a flash image is utilized as well as no-flash multiple exposure images to further reduce the noise. Multiple exposure integration is performed in a wavelet domain, where noise removal is achieved by the wavelet-shrinkage for multiple exposures. Our method works well especially for noise in shadows. We show the validity of the proposed algorithm by simulating the method with some actual noisy images.

Analysis of effect of noise removal filters on noisy remote sensing images

Abstract: In this work, we have applied various filters on remote sensing images for denoising them. The fundamental key challenge to noise reduction is to reduce or eliminate the noise without failing other aspects of the image. RS (Remote Sensing) Image denoising involves the manipulation of the image data to produce a visually high quality image. There are many kinds of noise that affect on remote sensing images but we have selected only impulsive noise i.e. Gaussian noise and Salt & Pepper Noise. In a simulation we took remote sensing images and analyzed it with an Average filter, Median filter, unsharp filter and Wiener Filter and using statistical quality measures. The analysis of effect of noise removal technique is given in this paper.

Automatic estimation of the noise model in fundus images

Abstract: For several years, a lot of studies have been done on image analysis and image understanding. In medical imaging, the retinal images are usually corrupted by noise in its acquisition or transmission. There are various sources of noise inherent in the use of CCD's (Charge Coupled Device) and other external effects such as space radiation (cosmic rays) can have negative effects on the obtained data. In the context of retinal image denoising, most of algorithms assumes the noise is additive and independent of the RGB image data, and is also a Gaussian sample. However, the type and level of the noise generated by digital cameras are unknown if we don't have enough informations about the sensor and circuitry of a digital camera. Therefore, these approaches cannot effectively recover the ”true” signal (or its best approximation) from these noisy acquired observations. Thus, modeling noise in retinal images is an important and huge step before any processing task. The purpose of this paper is to explore CCD's and understand an algorithm for estimating the noise model in fundus image.

Fast impulsive noise removal in color images

Abstract: In the paper a novel switching filter based on the reduced ordering statistics, combined with the peer group concept is proposed. The new technique assigns to each pixel the sum of the distances to two closest neighbors and if the sum exceeds a specified threshold, then the pixel is declared as corrupted and replaced, otherwise it is retained. The main advantage of the novel approach is its ability to suppress the noise component, while preserving fine image details. The proposed filtering framework is characterized by a very low computational complexity, which enables its adoption in real-time applications.

Apparatus and method for processing depth image

Abstract: A method and apparatus for processing a depth image that removes noise of a depth image may include a noise estimating unit to estimate noise of a depth image using an amplitude image, a super-pixel generating unit to generate a planar super-pixel based on depth information of the depth image and the noise estimated, and a noise removing unit to remove noise of the depth image using depth information of the depth image and depth information of the super-pixel.

Detection and removal of random-valued impulse noise from images using sparse representations

Abstract: This paper proposes a novel iterative method for the removal of random-valued impulse noise from the images using sparse representations. Each iteration has three stages. In the first stage, the positions of the possible noise pixels are detected using a sparse representation of the pixels in a window. In the next stage, the pixels that are detected as noisy pixels are treated as missing pixels and are filled using image inpainting through sparse representations. In the third stage, the pixels detected as noise pixels in the first stage are tested based on the inpainted value to determine the correctness of the noise detection at the first stage. In the subsequent iterations, the output of the previous iteration is considered to be the input for the detection and removal of the impulse noise.

Method for reducing noise in tomographic image and recording medium having noise reducing program stored therein

Abstract: A method for reducing noise in a tomographic image, includes: acquiring phase information of a noise component signal, the signal being provided as a signal corresponding to a noise component of the tomographic image and included in a spectrum interference signal output from a detector of a swept source optical coherence tomography device; acquiring a tomographic image in response to a spectrum interference signal with a corrected phase deviation based on the acquired phase information; and reducing the noise component of this tomographic image.

Mechanical noise suppression based on non-negative matrix factorization and multi-band spectral subtraction for digital cameras

Abstract: This paper proposes a new non-stationary noise suppression method to reduce the mechanical noise generated when audio signals are recorded with a digital camera. The proposed method first utilizes a non-negative matrix factorization (NMF) technique to estimate the noise spectrum of the mechanical noise from the noisy audio spectrum. After that, the mechanical noise contaminated in the audio signal is suppressed by multi-band spectral subtraction. In particular, the NMF technique estimates the noise spectrum in a frame-wise manner in order for the proposed method to operate in real-time. The performance of the proposed mechanical noise suppression method is evaluated in terms of log-spectral distortion, cepstral distortion, subjective quality, and computational complexity. In addition, it is compared with the performance of conventional methods. It is shown from the evaluation that the proposed method provides lower log-spectral and cepstral distortions with better subjective preference than conventional methods. Moreover, the complexity of the proposed method is low enough to be implemented on a commercially available digital camera.

Trimmed Non-local Means Technique for Mixed Noise Removal in Color Images

Abstract: In this paper a novel approach to the mixed noise removal in color images is proposed. The described method is a generalization of the Non-Local Means algorithm, where the pixels in the filtering window are ordered and only the most centrally located pixels in the filtering window are considered and used to calculate the weights needed for the averaging operation. The comparison with the existing state-of-the-art denoising schemes in terms of image restoration quality measures shows, that the new approach yields significantly better results in suppressing mixed noise in color digital images.

Enhanced Film Grain Noise Removal for High Fidelity Video Coding

Abstract: In this paper, we propose a novel technique for film grain noise removal, which can be adopted in high fidelity video coding. Film grain noise enhances the natural appearance of high fidelity video, therefore it is should be preserved. However, film grain noise is a burden to typical video compression systems because it has relatively large energy level in the high frequency region. In order to improve the coding performance while preserving film grain noise, the noise removal and synthesis process is used. We propose a film grain noise removal technology in the pre-processing step. In pre-processing step, film grain noise is removed by using temporal, spatial and inter-color correlation. Specially, color image denoisng using inter color prediction provides good denoising performance in noise concentrated B plane because film grain noise has inter-color correlation in the RGB domain. The results show that the coding gain of denoised video is higher than for previous works, while the visual quality of the final reconstructed video is well preserved.

An Advanced Technique for Removal Of Salt & Pepper Noise In Images

Abstract: Transmission of images are overcome channels, Due to unwanted communication the salt and pepper noise is occur in images. The word salt and pepper noise is also speaks out a Impulse noise. The filtering mainly used for removal of impulse noise or salt and pepper noise for noise free images and fully recovered by minimum signal distortion also uncorrupted the images. For best solutions of removal of salt and pepper noise is a nonlinear digital filters which is based on order statistics of median filter. The Median filters are remove noisy signal and unwanted signals without damaging the corners .Median filter are operates in low densities but not in higher densities because at higher the image are blurred and damage the image. The filtering leaves the uncorrupted pixels and accepts the corrupted pixel. Median filter is applied to image unconditionally to practiced of conventional schemes for alert the intensities of remove the noisy signal from image then the results between the corrupted and uncorrupted pixels are prior to applying nonlinear filtering is highly desirable in images. The process of "Adaptive Median "filter is to identifies the noisy images or pixels and then remove the noisy pixels and replace them at same position by using the median filters or its variants, where the remaining are same or unchanged. The Adaptive median filter is best for removal of noisy pixels at low level. But at high level noise the adaptive median filter is provide a large Window size it is not to fit the pixel. The Adaptive median filter is also known as "switching" and "decision based" system. The existing system are Robust Estimation Algorithm (REA), Adaptive Median Filter (AMF), Standard Median Filter(SMF), it shows best performances at low noise level and at high noise level bad. A new Weighted Median Filter (WMF) is best for high noise

Monochromatic-Based Method for Impulse Noise Detection and Suppression in Color Images

Abstract: A new method to detect and reduce the impulse noise in color images is presented in this paper. The method consists of two stages: detection and filtering. Since each of the individual channels (components) of the color image can be considered as a monochrome image, both stages are applied to each channel separately, and then the individual results are combined into one output image. The corrupted pixels are detected in the first stage based on a proposed innovative switching technique. The noise-free pixels are copied to their corresponding locations in the output image. In the second stage, average filtering is applied only to those pixels which are determined to be noisy in the first stage, and only noise-free pixel values are involved in calculating this average. The size of the sliding window depends on the estimated noise density and is very small even for high noise densities. The proposed method is effective in noise reduction while preserving edge details and color chromaticity. Simulation results show that the proposed method outperforms all the tested existing state-of-the-art methods used in digital color image restoration in both standard objective measurements and perceived image quality.

Noise reduction for image sequences

Abstract: Described herein are techniques related to noise reduction for image sequences or videos. This Abstract is submitted with the understanding that it will not be used to interpret or limit the scope and meaning of the claims. A noise reduction tool includes a motion estimator configured to estimated motion in the video, a noise spectrum estimator configured to estimate noise in the video, a shot detector configured to trigger the noise estimation process, a noise spectrum validator configured to validate the estimated noise spectrum, and a noise reducer to reduce noise in the video using the estimated noise spectrum.