Showing papers on "Dark-frame subtraction published in 2021"

Double Low-Rank Matrix Decomposition for Hyperspectral Image Denoising and Destriping

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.

Thermal drift compensation in dark-frame non-uniformity correction for an InGaAs PIN 3D flash lidar camera

Abstract: This paper presents the expansion of dark-frame non-uniformity correction (DFNUC) techniques to include compensation for thermal drift in a 128×128 PIN diode 3D flash LiDAR camera. Flash LiDAR cameras are operated in various climates, which makes thermal compensation necessary in the dark NUC algorithm. The thermal excitation of electrons has a significant effect on the dark current in an InGaAs PIN photodetector and on the CMOS readout circuitry, thus impacting the output image. This is a well-known phenomenon in imaging sensors and various algorithms have been established to address thermal drift. This paper adapts a linear model for dark signal calibration used in infrared cameras for the calibration of a PIN diode flash LiDAR camera in both intensity and range return. The experimental process involves collecting dark frames in increments of the internal camera temperature from 22°C to 36°C using thermoelectric (TE) cooling modules. A linear trendline is developed for each individual pixel based on the average frame return, which suppresses the random temporal noise and isolates the dark signal return. The trendline helps form a model for the dark frame offset as a function of temperature, which is used for the dark-frame NUC process. The dark-frame NUC with thermal drift compensation is then evaluated by correcting dark frames at various operating temperatures. Finally, illuminated scenes captured by the camera with a 5.91ns, 842.4μJ pulsed laser at 5Hz are corrected at multiple operation temperatures to show the effectiveness of the dark non-uniformity correction algorithm.

Dependence of SEUs in Digital Cameras on Pixel size and Elevation

Abstract: Determining how Integrated circuits' (ICs) soft (transient) errors (also known as Single Event Upsets or SEUs) change with elevation is important in many applications, both on Earth and in aerospace. We have been studying SEUs using camera sensors, which record SEUs as bright spots in a dark image. The most important advantage of camera pixels for this study is that they integrate the information over time until read out, creating a record of when/where cosmic ray particles collide with the IC, and the amount of the deposited charge. The study was done by collecting large numbers (100 's - 1000's) of images at exposures of ~30 sec. SEUs appear as brighter spots above the noise background of the sensor, typically 1 or 2 per image, that appear in a single dark frame within a stream of images. At higher camera gains (ISO) we can detect weaker SEUs but must deal with the noise at these long exposures. We use a Pixel Noise Distribution, obtained from the same set of images, to distinguish SEUs from the background noise. Previously we noted that the SEU rate/area changes with the elevation of the camera, as has been found in other ICs. We have measured this rate at elevations from 0 to 1100m, using cameras with pixel sizes from 4.1 to 6.5 microns at several gain (ISO) levels (as higher ISOs magnify weaker SEUs). We then compared our results to previously developed models of cosmic radiation changes with elevation.