Showing papers on "Dark-frame subtraction published in 2018"

Effective image differencing with convolutional neural networks for real-time transient hunting

Abstract: Large sky surveys are increasingly relying on image subtraction pipelines for real-time (and archival) transient detection. In this process one has to contend with varying point-spread function (PSF) and small brightness variations in many sources, as well as artefacts resulting from saturated stars and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artefacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image-subtraction pipeline – image registration, background subtraction, noise removal, PSF matching and subtraction – in a single real-time convolutional network. Once trained, the method works lightening-fast and, given that it performs multiple steps in one go, the time saved and false positives eliminated for multi-CCD surveys like Zwicky Transient Facility and Large Synoptic Survey Telescope will be immense, as millions of subtractions will be needed per night.

A multi-scale non-uniformity correction method based on wavelet decomposition and guided filtering for uncooled long wave infrared camera

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.

Denoising of sparse images in impulsive disturbance environment

Abstract: The paper presents a method for denoising and reconstruction of sparse images based on a gradient-descent algorithm. It is assumed that the original (non-noisy) image is sparse in the two-dimensional Discrete Cosine Transform (2D-DCT) domain. It is also assumed that a number of image pixels is corrupted by a salt and pepper noise. In addition, we assume that there are pixels corrupted by a noise of any value. In this paper we introduce a method to find the positions of the corrupted pixels when the noise is not of the salt and pepper form. The proposed algorithm for noisy pixels detection and reconstruction works blindly. It does not require the knowledge about the positions of corrupted pixels. The only assumption is that the image is sparse and that the noise degrades this property. The advantage of this reconstruction algorithm is that we do not change the uncorrupted pixels in the process of the reconstruction, unlike common reconstruction methods. Corrupted pixels are detected and removed iteratively using the gradient of sparsity measure as a criterion for detection. After the corrupted pixels are detected and removed, the gradient algorithm is employed to reconstruct the image. The algorithm is tested on both grayscale and color images. Additionally, the case when both salt and pepper noise and a random noise, within the pixel values range, are combined is considered. The proposed method can be used without explicitly imposing the image sparsity in a strict sense. Quality of the reconstructed image is measured for different sparsity and noise levels using the structural similarity index, the mean absolute error, mean-square error and peak signal-to-noise ratio and compared to the traditional median filter and recent algorithms, one based on the total-variations reconstruction and a two-stage adaptive algorithm.

A Speckle Noise Removal Method

Abstract: During formation of echocardiographic images, speckle noise is introduced, which diminishes important information present in an image and effects physician's capability to interpret image correctly. In the literature, many techniques have been proposed to remove unwanted noise from the image. In this paper, an intelligent denoising algorithm for echocardiographic images has been proposed, which first divides input image into different regions, namely smooth, texture and edge, using coefficient of variation. Fuzzy logic is used to draw boundaries between these image regions. Average filter and fractional integral filters are deployed to denoise pixels of various regions. Selection of filter depends on the characteristics of a region. The proposed technique improves quality of denoised image by suppressing maximum noise and producing no artifacts. Simulation results show superiority of proposed methodology over state-of-the-art existing methodologies, visually and using quantitative measures i.e. mean square error, peak signal to noise, edge preservation index, correlation coefficient and structure similarity.

Dark non-uniformity correction and characterization of a 3D flash lidar camera

Abstract: A partial calibration technique for a 128 × 128 pin diode 3D flash LIDAR camera is presented. This paper presents dark non-uniformity correction (NUC) of a 3D flash LIDAR camera using dark frame subtraction. Dark frames are taken near threshold for intensity return to generate simultaneous trigger on a flash LIDAR camera, with trigger ramp set to zero for both range and intensity returns. Frames are cropped to a region of interest (ROI) and concatenated ideal dark intensity and dark range return into dark frames, processed into calibration files with nearest neighbor correction in dark intensity frames to correct out slowly varying, high intensity temporal noise when operating near threshold. Results and validation of applied NUC on 3D flash LIDAR camera are presented. We characterize a 3D flash LIDAR camera with PIN diode architecture including range walk, gain characterization in both intensity and range domains. Characterization of 3D flash LIDAR imager was performed using a fiber laser operating at 1550 nm, 20 μJ energy per pulse, TTL triggering, a pulse generator to generate time delay necessary for triggering the laser from the camera ARM signal, and an attenuator for fine control of the output signal. Time delay is relative to the range domain, whereas output signal is relative to the intensity domain.

Image processing method capable of detecting noise and related navigation device

Abstract: An image processing method capable of detecting noise includes adjusting a lighting unit to acquire an over-exposure image, comparing each pixel of the over-exposure image with at least one threshold value, labeling a pixel of the over-exposure image as the noise while bright intensity of the pixel is lower than the threshold value, calculating a simulating value according to bright intensity of pixels around the noise and except the noise, and utilizing the simulating value and bright intensity of other pixels except the noise to execute a displacement detecting calculation.