11 research outputs found

    AAM: An Assessment Metric of Axial Chromatic Aberration

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    Knowledge of lens specifications is important to identify the best lens for a given capture scenario and application. Lens manufacturers provide many specifications in their data sheets, and multiple initiatives for testing and comparing different lenses can be found online. However, due to the lack of a suitable metric or technique, no evaluation of axial chromatic aberration is available. In this paper, we propose a metric, Axial Aberration Magnitude or AAM, that assesses the degree of axial chromatic aberration of a given lens. Our metric is generalizable to multispectral acquisition systems and is very simple and cheap to compute. We present the entire procedure and algorithm for computing the AAM metric, and evaluate it for two spectral systems and two consumer lenses

    Fourier-Domain Optimization for Image Processing

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    Image optimization problems encompass many applications such as spectral fusion, deblurring, deconvolution, dehazing, matting, reflection removal and image interpolation, among others. With current image sizes in the order of megabytes, it is extremely expensive to run conventional algorithms such as gradient descent, making them unfavorable especially when closed-form solutions can be derived and computed efficiently. This paper explains in detail the framework for solving convex image optimization and deconvolution in the Fourier domain. We begin by explaining the mathematical background and motivating why the presented setups can be transformed and solved very efficiently in the Fourier domain. We also show how to practically use these solutions, by providing the corresponding implementations. The explanations are aimed at a broad audience with minimal knowledge of convolution and image optimization. The eager reader can jump to Section 3 for a footprint of how to solve and implement a sample optimization function, and Section 5 for the more complex cases

    Deep Gaussian Denoiser Epistemic Uncertainty and Decoupled Dual-Attention Fusion

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    Following the performance breakthrough of denoising networks, improvements have come chiefly through novel architecture designs and increased depth. While novel denoising networks were designed for real images coming from different distributions, or for specific applications, comparatively small improvement was achieved on Gaussian denoising. The denoising solutions suffer from epistemic uncertainty that can limit further advancements. This uncertainty is traditionally mitigated through different ensemble approaches. However, such ensembles are prohibitively costly with deep networks, which are already large in size. Our work focuses on pushing the performance limits of state-of-the-art methods on Gaussian denoising. We propose a model-agnostic approach for reducing epistemic uncertainty while using only a single pretrained network. We achieve this by tapping into the epistemic uncertainty through augmented and frequency-manipulated images to obtain denoised images with varying error. We propose an ensemble method with two decoupled attention paths, over the pixel domain and over that of our different manipulations, to learn the final fusion. Our results significantly improve over the state-of-the-art baselines and across varying noise levels.Comment: Code and models are publicly available on https://github.com/IVRL/DE

    Near-Infrared Fusion for Photorealistic Image Dehazing

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    Scattering of light due to the presence of aerosol particles along the path of radiation causes atmospheric haze in images. This scattering is significantly less severe in longer wavelength bands than in shorter ones, thus the importance of near-infrared (NIR) information for dehazing color images. This paper first presents an adaptive hyperspectral al- gorithm that analyzes intensity inconsistencies across spectral bands. It then leverages the algorithm’s results to preserve photorealism of the visible color image during the dehazing. The color images are dehazed through a hyperspectral fusion of color and NIR images, taking into account any inconsistencies that can affect the photorealism. Our dehazing results on real images contain no halo or aliasing artifacts in hazy regions and successfully preserve the color image elsewhere

    Computational Imaging Approach to Recovery of Target Coordinates Using Orbital Sensor Data

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    This dissertation addresses the components necessary for simulation of an image-based recovery of the position of a target using orbital image sensors. Each component is considered in detail, focusing on the effect that design choices and system parameters have on the accuracy of the position estimate. Changes in sensor resolution, varying amounts of blur, differences in image noise level, selection of algorithms used for each component, and lag introduced by excessive processing time all contribute to the accuracy of the result regarding recovery of target coordinates using orbital sensor data. Using physical targets and sensors in this scenario would be cost-prohibitive in the exploratory setting posed, therefore a simulated target path is generated using Bezier curves which approximate representative paths followed by the targets of interest. Orbital trajectories for the sensors are designed on an elliptical model representative of the motion of physical orbital sensors. Images from each sensor are simulated based on the position and orientation of the sensor, the position of the target, and the imaging parameters selected for the experiment (resolution, noise level, blur level, etc.). Post-processing of the simulated imagery seeks to reduce noise and blur and increase resolution. The only information available for calculating the target position by a fully implemented system are the sensor position and orientation vectors and the images from each sensor. From these data we develop a reliable method of recovering the target position and analyze the impact on near-realtime processing. We also discuss the influence of adjustments to system components on overall capabilities and address the potential system size, weight, and power requirements from realistic implementation approaches
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