Topic
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.
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the authors present data for the dark current of a commercially available CMOS image sensor for different gain settings and bias offsets over the temperature range of 295 to 340 K and exposure times of 0 to 500 ms.
Abstract: We present data for the dark current of a commercially available CMOS image sensor for different gain settings and bias offsets over the temperature range of 295 to 340 K and exposure times of 0 to 500 ms. The analysis of hot pixels shows two different sources of dark current. One source results in hot pixels with high but constant count for exposure times smaller than the frame time. Other hot pixels exhibit a linear increase with exposure time. We discuss how these hot pixels can be used to calculate the dark current for all pixels. Finally, we show that for low bias settings with universally zero counts for the dark frame one still needs to correct for dark current. The correction of thermal noise can therefore result in dark frames with negative pixel values. We show how one can calculate dark frames with negative pixel count.
30 citations
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31 Dec 1996TL;DR: In this paper, a system and method for noise filtering digital images is implemented in an automated image enhancement system without significantly reducing the performance of the automated image enhancing system, based on image measurements.
Abstract: A system and method for noise filtering digital images is implemented in an automated image enhancement system without significantly reducing the performance of the automated image enhancement system. The system and method trigger a noise filter based on image measurements. If the image measurements indicate that the likelihood of objectionable "noise" in the image is high, a noise filter is triggered. Otherwise the noise filter is not triggered. In this way, only images that are in need of noise filtering are filtered, while other images are processed without the additional performance overhead required by the noise filter.
30 citations
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TL;DR: Wang et al. as mentioned in this paper proposed an effective non-uniformity correction (NUC) method to remove strip noise without loss of fine image details in uncooled long-wave infrared imaging systems.
Abstract: In uncooled long-wave infrared (LWIR) imaging systems, non-uniformity of the amplifier in readout circuit will generate significant noise in captured infrared images. This type of noise, if not eliminated, may manifest as vertical and horizontal strips in the raw image and human observers are particularly sensitive to these types of image artifacts. In this paper we propose an effective non-uniformity correction (NUC) method to remove strip noise without loss of fine image details. This multi-scale destriping method consists of two consecutive steps. Firstly, wavelet-based image decomposition is applied to separate the original input image into three individual scale levels: large, median and small scales. In each scale level, the extracted vertical image component contains strip noise and vertical-orientated image textures. Secondly, a novel multi-scale 1D guided filter is proposed to further separate strip noise from image textures in each individual scale level. More specifically, in the small scale level, we choose a small filtering window for guided filter to eliminate strip noise. On the contrary, a large filtering window is used to better preserve image details from blurring in large scale level. Our proposed algorithm is systematically evaluated using real-captured infrared images and the quantitative comparison results with the state-of-the-art destriping algorithms demonstrate that our proposed method can better remove the strip noise without blurring image fine details.
29 citations
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19 Aug 2001TL;DR: In this article, the authors proposed a speech enhancement technique based on the spectral subtraction method, which is one of the major techniques of speech enhancement, but the enhanced output speech signal of the spectral subtraction method is corrupted by "musical noise".
Abstract: We propose a speech enhancement technique. It is based on the spectral subtraction method, which is one of the major techniques of speech enhancement. However, the enhanced output speech signal of the spectral subtraction method is corrupted by "musical noise". The musical noise is an offensive noise for human listening. To reduce the musical noise, we adopt an iterative algorithm. The iterative algorithm is derived from the same idea as Wiener filtering for speech enhancement. We use the output signal of the spectral subtraction method as the input signal again. This process is iterated a few times. Each time we iterate the spectral subtraction method, we estimate the noise signal and subtract it. Therefore we can further reduce the musical noise with each iteration.
29 citations
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TL;DR: Experimental results show that the new fuzzy filter for the removal of random impulse noise in digital grayscale image sequences outperforms other state-of-the-art filters in terms of the peak-signal-to-noise ratio as well as visual quality.
29 citations