Dark-frame subtraction

About: Dark-frame subtraction is a research topic. Over the lifetime, 1216 publications have been published within this topic receiving 20763 citations.

A fast noise variance estimation algorithm

Abstract: Noise estimation is an important part of video and image processing. In real time denoising of CMOS sensor environment, it is especially required to achieve fast and accurate noise estimation. This paper presents a fast and efficient noise estimation algorithm. The algorithm is block-based, in which noise of image or video is assumed to be additive zero-mean Gaussian noise. The algorithm requires N1 × N1 window and the corresponding operation of computing variance. In order to achieve fast and accurate noise estimation, the method we use can avoid the sorting process making the complexity order of the algorithm down to O(n) from either O(n2) or O(nlog 2 n). In order to improve the accuracy of this algorithm, the main parameters in this paper are set to be adjusted adaptively according to the content of images or videos. By conducting experiments we find that the algorithm is fast and accurate.

Denoising X-ray CT images based on product Gaussian mixture distribution models for original and noise images

Abstract: An adaptive Wiener filter for denoising X-ray CT image has been proposed based on the universal Gaussian mixture distribution model (UNI-GMM). In this method, the UNI-GMM is estimated by the statistical learning method using two sets of pair images, one of which is an observed (low dose) X-ray CT image set and the other is an original (high dose) X-ray CT image set. Owing to the physical limitations of CT scanners, the original (high dose) X-ray CT image also includes considerable noise that prevented precise learning of the UNI-GMM. On the other hand, the noise included in the X-ray CT images is the specific artifact which is called streak artifact and is known to be statistically non-stationary. In the previously proposed method, the artifact is treated to be stationary for simplicity. Thus the restored images include residual noise due to the non-stationary noise. In this paper, the UNI-GMM method is improved by a two stages product modeling. First, the UNI-GMM for the original image is estimated using a low noise natural image set that include scenes, portraits and still pictures, to prevent the effect of noise on the original (high dose) CT images. Second, the UNI-GMM for the noise image is estimated using a noise image set casted by subtracting the original X-ray CT images from the observed X-ray CT images. Simulation results show that the proposed product UNI-GMMs performs better than the conventional stationary noise model simply learned using X-ray CT images.

Analysis and improved preprocessing method of space noise in infrared image

Abstract: The components of the space noise in an infrared image with the sky background were discussedThe reasons for forming all types of noises were analysedThe noise types of the IR image were classified and their effects on the image were investigatedThe analysis indicates that the space nosie in the IR image mainly represents the Gauss noise and spiced-salt noiseAn improved median filtering based on a simple threshold is introduced by considering the difference in the noise disposal of the averaging method and the median filteringFor the different thresholds,the operators are different tooThe weight selection of every operator in the M×N area depends on the gray median in the areaThe more the gray value is close to the median,the more the weight of operator isThe background of the image becomes smoother after the preprocessingThe SNR and improved PSNR increase by 12dBIt indicates that the improved median filtering works better than the traditional ones

Development of denoising method for digital image in low-light condition

Abstract: A good noise reduction is a method that can reduce the noise level and preserve the details of the image. This paper proposes the development of a denoising method through hybridization of bilateral filters and wavelet thresholding for digital images in low light condition.In the first stage, the noisy image is passed through Bilateral Filter. However, only some amount of noise gets reduced and the image gives a blurred appearance. Hence to preserve the edge details and reduce the blur effect, Wavelet Thresholding is applied and it is proved that the proposed method is more successful to preserve the edges while suppressing the noise.

Design and implementation of adaptive Gaussian filters for the removal of salt and pepper noise on FPGA

Abstract: In recent eras of image processing, during image acquisition and transfer, images are often corrupted by impulse noise which is a major factor affecting the contents of a digital image. In fixed-value impulse noise, the gray value is a fixed value, i.e., either 0 or 255 (example: Salt and pepper noise). Digital signal processing frequently involves some method for noise reduction over an image. The median filter is a non-linear digital filter, which is most popularly used to remove impulse noises. Many popular algorithms were presented which eliminates impulse noises present and maintains the fine details of the image. But, median filter fails to preserves the edges of the image by uniform modification of the noise affected pixels and the noise-free pixels. Also, the conventional filters work better only over images affected with low noise ratios and is very poor when the noise ratio reaches above 40%. The proposed de-noising algorithm uses an adaptive Gaussian filters for removal of salt and pepper noise. As conventional de-noising algorithm fails at high noise density and in preserving edges along with image details the proposed technique switches between mask sizes depending noise density. To preserve edges and details of the image Adaptive threshold is employed. Advantage of adaptive threshold filtering does not affect the edges or other small structures in the image. Hence this method is more efficient for the images with very high noise ratio and preserving edges.