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 Restoration of the Image Contaminated with the Photon Shot Noise by an M-Transform Method

Abstract: We propose a novel approach for the photon shot noise reduction in image sensors inspired from an M-transform method. In our proposed method, the photon shot noise is transfered to small amplitude random signals. Then the small amplitude random signals are removed by the e filter. The Sobel method was used for the edge detection and the edge was preserved by not applying the e filter into the edge. After the noise reduction, the signals are restored to the image by a reverse M-transform. We compared our method with the smoothing filter, the median filter and the Wiener filter. In the photon shot noise according to the Poisson distribution, we changed the incidence number of photons and created the noise image by the simulation. In cases where the number of the maximum incidence photons was 128 pieces (illumination of about 2 lux), the proposal technique indicated the PSNR (Peak-Signal to Noise Ratio) value better than the Wiener filter. Furthermore, the proposal technique confirmed better than the smoothing filter subjectively according to the effect of edge preservation also under dark environment. As the result, our method is confirmed effective for the photon shot noise reduction.

Image Filtering Algorithm Using Double Noise Detector and Edge-Preserving Regularization Function

Abstract: The major drawback of recent image filtering algorithms for removing impulse noise is lack of the ability of preserving the image details and edges which are smaller than the size of filtering windows.To alleviate this limitation,a new image filtering algorithm using a double noise detector and edge-preserving regularization function is proposed in this paper.The proposed filter has a two-stage scheme:detecting noise and removing noise.In order to improve accurate rate of noise detection,noise candidates identified with the noise detection algorithm of the adaptive median filter are judged again by local fuzzy membership function,and then a convex objective function composed of 1 data-fidelity term and edge-preserving regularization function is employed to deal with noise candidates.The input of edge-preserving regularization function is adaptively selected to take full advantage of local features of the image.The image corrupted by noise is restored successfully as the convex objective function gets its minimum.Experimental results show the superiority of the proposed filter in terms of the ability of removing noise and preserving the details and edges of the image in comparison with some recent methods,it is also shown that even at a very high noise level(70%) details and edges of the original image are preserved very well with our method.

Improved switching- and counting-based filter for removing impulse noise from the color image

Abstract: A novel switching-based filter is proposed to suppress the impulse noise and preserve the fine details in the color image. The filtering method is divided into two stages, the noise detection and the noise cancellation. In the first stage of noise detection, pixels with similar color components in many neighboring positions are counted to see whether the current pixel is corrupted by impulse noise. In the second stage of noise cancellation, the previous outputs and the detected noise-free pixels are chosen as the samples of the vector median filter operation to improve the performance on suppressing impulse noise. Several experimental results have demonstrated that the proposed filter outperforms many filters in terms of both noise suppression and detail preservation.

A high-sensitivity 2x2 multi-aperture color camera based on selective averaging

Abstract: To demonstrate the low-noise performance of the multi-aperture imaging system using a selective averaging method, an ultra-high-sensitivity multi-aperture color camera with 2×2 apertures is being developed. In low-light conditions, random telegraph signal (RTS) noise and dark current white defects become visible, which greatly degrades the quality of the image. To reduce these kinds of noise as well as to increase the number of incident photons, the multi-aperture imaging system composed of an array of lens and CMOS image sensor (CIS), and the selective averaging for minimizing the synthetic sensor noise at every pixel is utilized. It is verified by simulation that the effective noise at the peak of noise histogram is reduced from 1.44 e- to 0.73 e- in a 2×2-aperture system, where RTS noise and dark current white defects have been successfully removed. In this work, a prototype based on low-noise color sensors with 1280×1024 pixels fabricated in 0.18um CIS technology is considered. The pixel pitch is 7.1μm×7.1μm. The noise of the sensor is around 1e- based on the folding-integration and cyclic column ADCs, and the low voltage differential signaling (LVDS) is used to improve the noise immunity. The synthetic F-number of the prototype is 0.6.

Sparsity-driven multiplicative noise reduction

Abstract: Speckle noise formed in Synthetic Aperture Radar (SAR) images makes visual and automatic analyses complicated. Thus, reducing speckle noise in homogeneous regions while preserving features such as edges and point scatterers is important as a pre-processing step. Although SAR images predominantly contains multiplicative noise, it also contains low amount of additive noise. In this study, a sparsity-driven multiplicative noise reduction method is proposed that also take additive noise component into account. Performance of the proposed methods is shown on SAR images.