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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25 Sep 2006TL;DR: In this article, a method and circuit for suppressing the generation of unnatural vertical streaks in output image data is proposed, where a detection processing circuit generates a first noise correction value based on first and second noise detection signals from an OB region.
Abstract: A method and circuit for suppressing the generation of unnatural vertical streaks in output image data. A detection processing circuit generates a first noise correction value based on first and second noise detection signals from an OB region. A correction processing circuit performs an offset process on a first noise correction value to generate a second noise correction value and performs an FIR filter process on the second noise correction value to generate a noise correction signal NC. The correction processing circuit then corrects the effective image signal from the effective image region using the noise correction signal and performs a horizontal LPF process on the corrected effective image signal to generate output image data.
12 citations
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TL;DR: In this article, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed, which is performed as a two phase process.
Abstract: Restoration of images corrupted by impulse noise is a very active research area in image processing. In this paper, an Efficient Adaptive Weighted Switching Median filter for restoration of images that are corrupted by high density impulse noise is proposed. The filtering is performed as a two phase process—a detection phase followed by a filtering phase. In the proposed method, noise detection is done by HEIND algorithm proposed by Duan et al. The filtering algorithm is then applied to the pixels which are detected as noisy by the detection algorithm. All uncorrupted pixels in the image are left unchanged. The filtering window size is chosen adaptively depending on the local noise distribution around each corrupted pixels. Noisy pixels are replaced by a weighted median value of uncorrupted pixels in the filtering window. The weight value assigned to each uncorrupted pixels depends on its closeness to the central pixel.
12 citations
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TL;DR: Experimental and data-processing procedures are developed which yield a subjective noise sensitivity function of noise frequency for monochrome low resolution video pictures and two noise sensitivity functions are obtained using these experimental methods.
Abstract: A model for the subjective effects of noise on television picture quality is described. Experimental and data-processing procedures are developed from this model which yield a subjective noise sensitivity function of noise frequency for monochrome low resolution video pictures. The results obtained apply to high levels of noise power. Two noise sensitivity functions are obtained using these experimental methods, one for still pictures and one for live pictures.
12 citations
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15 Feb 2007
TL;DR: In this paper, the authors proposed an image information acquisition section 101 acquires image information, a component separation section 102 separates the acquired image information into luminance and chrominance information, and a luminance component noise elimination section 105 eliminates noise of the luminance information by using a first noise elimination method, and then a chrominance component noise eliminating section 106 eliminates the chrominance noise using a second noise elimination technique.
Abstract: PROBLEM TO BE SOLVED: To provide an image processing apparatus capable of executing effective noise elimination with less blurred edges, an imaging apparatus, an image processing method, and an image processing program. SOLUTION: An image information acquisition section 101 acquires image information, a component separation section 102 separates the acquired image information into luminance information and chrominance information, a luminance component noise elimination section 105 eliminates noise of the luminance information by using a first noise elimination method, and a chrominance component noise elimination section 106 eliminates noise of the chrominance information by using a second noise elimination method different from the first noise elimination method employed by the luminance component noise elimination section 105. COPYRIGHT: (C)2007,JPO&INPIT
12 citations
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TL;DR: In this article , a double low-rank (DLR) matrix decomposition method was proposed for HSI denoising and destriping. But the proposed DLR model cannot completely remove stripe noise when the stripe noise is no longer sparse.
Abstract: Hyperspectral images (HSIs) have a wealth of applications in many areas, due to their fine spectral discrimination ability. However, in the practical imaging process, HSIs are often degraded by a mixture of various types of noise, for example, Gaussian noise, impulse noise, dead pixels, dead lines, and stripe noise. Low-rank matrix decomposition theory has been widely used in HSI denoising, and has achieved competitive results by modeling the impulse noise, dead pixels, dead lines, and stripe noise as sparse components. However, the existing low-rank-based methods for HSI denoising cannot completely remove stripe noise when the stripe noise is no longer sparse. In this article, we extend the HSI observation model and propose a double low-rank (DLR) matrix decomposition method for HSI denoising and destriping. By simultaneously exploring the low-rank characteristic of the lexicographically ordered noise-free HSI and the low-rank structure of the stripe noise on each band of the HSI, the two low-rank constraints are formulated into one unified framework, to achieve separation of the noise-free HSI, stripe noise, and other mixed noise. The proposed DLR model is then solved by the augmented Lagrange multiplier (ALM) algorithm efficiently. Both simulation and real HSI data experiments were carried out to verify the superiority of the proposed DLR method.
12 citations