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.

Adaptive suppression of CCD signal-dependent noise in light space

Abstract: We propose a denoising scheme to restore images degraded by CCD noise. Typically, restoration algorithms assume a linear mapping between the incident light space and image space. However, in practice, a camera response function performs a non-linear mapping on the sensor output and, as a result, the sensor noise model becomes more complex in the image space. We correct for non-linearity by mapping the corrupted image into "light space", where the relationship between the incident light and light space values is linear. To reduce the sensor noise we accurately model the CCD sensor noise by using the photon transfer curve. We then develop a combination of adaptive filters based on the estimated noise model in light space. Our adaptive system demonstrates efficient noise removal performance in uniform regions, while preserving edges and fine details.

A new edge preserved technique using iterative median filter

Abstract: Median filters are known to overtake adaptive median filters in the removal of impulse noise due to their high and low amount of the pixel values. In this paper, we propose two modifications to improve the image using DTBID and EPIF concepts. The iterative median filter performance shows the preserved eMedian filters are known to overtake adaptive median filters in the removal of impulse noise due to their high and low amount of the pixel values. In this paper, we propose two modifications to improve the image using DTBID and EPIF concepts. The iterative median filter performance shows the preserved edges and also removing the random valued impulse noise using proposed DTBID method. Extremist analysis shows the fight of the would-be modifications in producing enhanced images than the DTBID processes.dges and also removing the random valued impulse noise using proposed DTBID method. Extremist analysis shows the fight of the would-be modifications in producing enhanced images than the DTBID processes.

Method and apparatus for removing noise data from a digitized image

Abstract: An apparatus and method for removing background noise and high frequency noise form an image by comparing each pixel in the image with neighboring pixels defining a variably shaped and sized kernel. The size and shape of the kernel are optimized for the particular characteristics of the data to be analyzed.

No-reference image noise estimation based on noise level accumulation

Abstract: In this paper, a method of no-reference image noise assessment is presented, which utilizes the estimated noise level accumulation (NLA) index value. The affine reconstruction model is applied after segmenting the noisy image into several patches. Boundary blur process is conducted to smooth the segmentation edges. For each image patch the mean value standing for brightness and the standard deviation value indicating the noise standard deviation are computed to give the noise samples estimation. The accurate image noise standard deviation is estimated by integrating NLA index value of several overlapped intervals combined with different visual weights. Experiment results are provided to demonstrate that the proposed method performs well for images with different contents over a large range of noise levels both monotonously and accurately. Comparisons against other conventional approaches are also carried out to exhibit the superior performance of the proposed algorithm.

Image de-noising by dual threshold median filtering for random valued impulse noise

Abstract: In the field of digital image processing[4], noise removal is always a critical process In this paper we proposed an enhanced method of image de-noising The purpose of this new method is to improve the signal to noise ratio (SNR) of de-noised image and get more better image, especially when image corrupted by high noise density We improved the median filter algorithm, and get comparatively better results than previous methods The mathematical analysis shows that this process improve the PSNR[8] (Peak signal to noise ratio) at high density noise level This method used the combination of median and average functions Two levels of threshold and improved median value remove the noise much effectively The mathematical analysis shows that the analysis of the noisy pixels and use of noise-free pixels for de-noising purpose produces much better results and gives better quality de-noised version