Infrared cameras can directly measure the apparent temperature of objects, providing thermal imaging. However, the raw output from most infrared cameras suffers from a strong, often limiting noise source called nonuniformity. Manufacturing imperfections in infrared focal planes lead to high pixel-to-pixel sensitivity to electronic bias, focal plane temperature, and other effects. In turn, different pixels within the focal plane array give a drastically different electronic response to the same irradiance. The resulting imagery can only provide useful thermal imaging after a nonuniformity calibration has been performed. Traditionally, these calibrations are performed by momentarily blocking the field of view with a flat temperature plate or blackbody cavity. However because the pattern is a coupling of manufactured sensitivities with operational variations, periodic recalibration is required, sometimes on the order of tens of seconds. A class of computational methods called Scene-Based Nonuniformity Correction (SBNUC) has been researched for over 20 years where the nonuniformity calibration is estimated in digital processing by analysis of the video stream in the presence of camera motion. The most sophisticated SBNUC methods can completely and robustly eliminate the high-spatial frequency component of nonuniformity with only an initial reference calibration or potentially no physical calibration. I will demonstrate a novel algorithm that advances these SBNUC techniques to support all spatial frequencies of nonuniformity correction. Long-wave infrared microgrid polarimeters are a class of camera that incorporate a microscale per-pixel wire-grid polarizer directly affixed to each pixel of the focal plane. These cameras have the capability of simultaneously measuring thermal imagery and polarization in a robust integrated package with no moving parts. I will describe the necessary adaptations of my SBNUC method to operate on this class of sensor as well as demonstrate SBNUC performance in LWIR polarimetry video collected on the UA mall.

<p>Denna uppsats syftar till att undersöka användbarheten av metoden Independent Component Analysis (ICA) för brusreducering av bilder tagna av infraröda kameror. Speciellt fokus ligger på att reducera additivt brus. Bruset delas upp i två delar, det Gaussiska bruset samt det sensorspecifika mönsterbruset. För att reducera det Gaussiska bruset används en populär metod kallad sparse code shrinkage som bygger på ICA. En ny metod, även den byggandes på ICA, utvecklas för att reducera mönsterbrus. För varje sensor utförs, i den nya metoden, en analys av bilddata för att manuellt identifiera typiska mönsterbruskomponenter. Dessa komponenter används därefter för att reducera mönsterbruset i bilder tagna av den aktuella sensorn. Det visas att metoderna ger goda resultat på infraröda bilder. Algoritmerna testas både på syntetiska såväl som på verkliga bilder och resultat presenteras och jämförs med andra algoritmer.</p> / <p>The purpose of this thesis is to evaluate the applicability of the method Independent Component Analysis (ICA) for noise reduction of infrared images. The focus lies on reducing the additive uncorrelated noise and the sensor specific additive Fixed Pattern Noise (FPN). The well known method sparse code shrinkage, in combination with ICA, is applied to reduce the uncorrelated noise degrading infrared images. The result is compared to an adaptive Wiener filter. A novel method, also based on ICA, for reducing FPN is developed. An independent component analysis is made on images from an infrared sensor and typical fixed pattern noise components are manually identified. The identified components are used to fast and effectively reduce the FPN in images taken by the specific sensor. It is shown that both the FPN reduction algorithm and the sparse code shrinkage method work well for infrared images. The algorithms are tested on synthetic as well as on real images and the performance is measured.</p>

Denna uppsats syftar till att undersöka användbarheten av metoden Independent Component Analysis (ICA) för brusreducering av bilder tagna av infraröda kameror. Speciellt fokus ligger på att reducera additivt brus. Bruset delas upp i två delar, det Gaussiska bruset samt det sensorspecifika mönsterbruset. För att reducera det Gaussiska bruset används en populär metod kallad sparse code shrinkage som bygger på ICA. En ny metod, även den byggandes på ICA, utvecklas för att reducera mönsterbrus. För varje sensor utförs, i den nya metoden, en analys av bilddata för att manuellt identifiera typiska mönsterbruskomponenter. Dessa komponenter används därefter för att reducera mönsterbruset i bilder tagna av den aktuella sensorn. Det visas att metoderna ger goda resultat på infraröda bilder. Algoritmerna testas både på syntetiska såväl som på verkliga bilder och resultat presenteras och jämförs med andra algoritmer. / The purpose of this thesis is to evaluate the applicability of the method Independent Component Analysis (ICA) for noise reduction of infrared images. The focus lies on reducing the additive uncorrelated noise and the sensor specific additive Fixed Pattern Noise (FPN). The well known method sparse code shrinkage, in combination with ICA, is applied to reduce the uncorrelated noise degrading infrared images. The result is compared to an adaptive Wiener filter. A novel method, also based on ICA, for reducing FPN is developed. An independent component analysis is made on images from an infrared sensor and typical fixed pattern noise components are manually identified. The identified components are used to fast and effectively reduce the FPN in images taken by the specific sensor. It is shown that both the FPN reduction algorithm and the sparse code shrinkage method work well for infrared images. The algorithms are tested on synthetic as well as on real images and the performance is measured.

<p>This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences. Fixed pattern noise is the dominantnoise component for many infrared detector systems, perceived as a superimposed pattern that is approximately constant for all image frames. </p><p>Primarily, methods based on estimation of the movement between individual image frames are studied. Using scene-matching techniques, global motion between frames can be successfully registered with sub-pixel accuracy. This allows each scene pixel to be traced along a path of individual detector elements. Assuming a static scene, differences in pixel intensities are caused by fixed pattern noise that can be estimated and removed. </p><p>The algorithms have been tested by using real image data from existing infrared imaging systems with good results. The tests include both a two-dimensional focal plane array detector and a linear scanning one-dimensional detector, in different scene conditions.</p>

This thesis describes and evaluates a number of algorithms for reducing fixed pattern noise in image sequences. Fixed pattern noise is the dominantnoise component for many infrared detector systems, perceived as a superimposed pattern that is approximately constant for all image frames. Primarily, methods based on estimation of the movement between individual image frames are studied. Using scene-matching techniques, global motion between frames can be successfully registered with sub-pixel accuracy. This allows each scene pixel to be traced along a path of individual detector elements. Assuming a static scene, differences in pixel intensities are caused by fixed pattern noise that can be estimated and removed. The algorithms have been tested by using real image data from existing infrared imaging systems with good results. The tests include both a two-dimensional focal plane array detector and a linear scanning one-dimensional detector, in different scene conditions.

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Logarithmic sensors are capable of capturing the wide dynamic range of intensities available in nature with minimum number of bits and post-processing required. A simple circuit able to perform logarithmic capture is one utilising a MOS device in weak inversion. However, the output of this pixel is crippled due to fixed pattern noise. Technique proposed to reduce this noise fail to produce high quality images on account of unaccounted high gain variations in the pixel. An electronic calibration technique is proposed which is capable of reducing both multiplicative as well as additive FPN. Contrast properties matching that of human eye are reported from these sensors. With reduced FPN, the pixel performance at low intensities becomes concerning. In these regions, the high leakage current of the CMOS process affects the logarithmic pixel. To reduce this current, two different techniques using a modified circuit and another with modified layout are tested. The layout technique is observed to reduce the leakage current. In addition, this layout can be used to linearise the output of logarithmic pixel in low light regions. The unique linear response at low light and logarithmic pixel at high light is further investigated. A new model based on the device physics is derived to represent this response. The fixed pattern noise profile is also investigated. An intelligent iterative scheme is proposed and verified to extract the photocurrent flowing in the pixel and correct the fixed pattern noise utilising the new model. Future research ideas leading to better designs of logarithmic pixels and post-processing of these signals are proposed at the end of the thesis.

This thesis provides theoretical and practical solutions to two problems raised by digital photography of moving scenes, and infrared photography. Until recently photographing moving objects could only be done using short exposure times. Yet, two recent groundbreaking works have proposed two new designs of camera allowing arbitrary exposure times. The flutter shutter of Agrawal et al. creates an invertible motion blur by using a clever shutter technique to interrupt the photon flux during the exposure time according to a well chosen binary sequence. The motion-invariant photography of Levin et al. gets the same result by accelerating the camera at a constant rate. Both methods follow computational photography as a new paradigm. The conception of cameras is rethought to include sophisticated digital processing. This thesis proposes a method for evaluating the image quality of these new cameras. The leitmotiv of the analysis is the SNR (signal to noise ratio) of the image after deconvolution. It gives the efficiency of these new camera design in terms of image quality. The theory provides explicit formulas for the SNR. It raises two paradoxes of these cameras, and resolves them. It provides the underlying motion model of each flutter shutter, including patented ones. A shorter second part addresses the the main quality problem in infrared video imaging, the non-uniformity. This perturbation is a time-dependent noise caused by the infrared sensor, structured in columns. The conclusion of this work is that it is not only possible but also efficient and robust to perform the correction on a single image. This permits to ensure the absence of ''ghost artifacts'', a classic of the literature on the subject, coming from inadequate processing relative to the acquisition model.

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