The reliability of solid-state image sensors is limited by the development of defects, particularly hot-pixels, which we have previously shown develop continuously over the sensor lifetime. Our statistical analysis based on the distribution and development date of defects concluded that defects are not caused by single traumatic incident or material failure, but rather by an external process such as radiation. This paper describes an automated process for extracting defect temporal growth data, thereby enabling a very wide sample of cameras to be examined and studied. The algorithm utilizes Bayesian statistics to determine the presence and absence of defects by searching through sets of color photographs. Monte Carlo simulations on a set of images taken at 0.06 to 0.5sec exposures demontrating that our tracing algorithm is able to pinpoint the defect development date for all the identified hot pixels within ±2 images. Although a previous study has shown that in-field defects are isolated from each other, image processing functions applied by cameras such as the demosaicing algorithm were found to casue a single defective pixel to appear as a cluster in a color image, increasing the challenge pinpointing the exact location of hot defects.
Although solid-state image sensors are known to develop defects in the field, little information is available about the
nature, quantity or development rate of these defects. We report on and algorithm and calibration tests, which
confirmed the existence of significant quantities of in-field defects in 4 out of 5 high-end digital cameras. Standard hot
pixels were identified in all 4 cameras. Stuck hot pixels, which have not been described previously, were identified in 2
cameras. Previously, hot-pixels were thought to have no impact at short exposure durations, but the large offset of stuck
hot pixels will degrade almost any image and cannot be ignored. Fully-stuck and abnormal sensitivity defects were not
found. Spatial investigation found no clustering. We tracked hot pixel growth over the lifetime of one camera, using
only normal photographs. We show that defects develop continually over the lifetime of the sensor, starting within
several months of first use, and do not heal over time. Our success in tracing the history of each defect confirms the
feasibility of using automatic defect identification to analyze defect response and growth characteristics in a multitude
of cameras already in the field, without performing additional experiments or requiring physical access to the cameras.
As digital imagers continue to increase in size and pixel density, the detection of faults in the field becomes critical to delivering high quality output. Traditional schemes for defect detection utilize specialized hardware at the time of manufacture and are impractical for use in the field, while previously proposed software-based approaches tend to lead to quality-degrading false positive diagnoses. This paper presents an algorithm that utilizes statistical information extracted from a sequence of normally captured images to identify the location and type of defective pixels. Building on previous research, this algorithm utilizes data local to each pixel and Bayesian statistics to more accurately infer the likelihood of each defect, which successfully improves the detection time. Several defect types are considered, including pixels with one-half of the typical sensitivity and permanently stuck pixels. Monte Carlo simulations have shown that for defect densities of up to 0.5%, 50 ordinary images are sufficient to accurately identify all faults without falsely diagnosing good pixels as faulty. Testing also indicates that the algorithm can be extended to higher resolution imagers and to those with noisy stuck pixels, with only minimal cost to performance.
Optical tomography within highly scattering media has usually employed coherence domain and time domain imaging, which observe the shortest path photons over the dominant randomly scattered background light. An alternative, Angular Domain Imaging, employs micromachined collimators which detect photons within a small angle of the aligned laser light source. These angular filters consist of micromachined silicon collimator channels 51 micron wide by 10 mm long on 102 micron spacing giving an acceptance angle of 0.29 degrees at a CCD detector. Phantom test objects were observed in turbid mediums ranging from 1 to 5 cm thick at effective scattered to ballistic ratios from 1:1 to greater than 100,000,000:1. Simple line and space test objects detection limits are set by detector pixel size not collimator hole spacing. Restricting the light emission to only the collimating array hole area provides increased detectability by reducing the amount of scattered light background. This is best done using cylindrical spherical cylindrical lens beam expanders/shrinkers to create a wide line of light of small thickness aligned to the collimator array. As object locations within the medium are moved closer to the detector/collimator, image detectability appears to depend on the scattering ratio after the test object rather than the total medium scattering. Hence, objects located closer to the detector than the middle of the medium are observed at a much higher scattering levels than those nearer the light source.