Many deblurring and blur kernel estimation methods use a maximum a posteriori approach or deep learning-based classification techniques to sharpen an image and/or predict the blur kernel. We propose a regression approach using convolutional neural networks (CNNs) to predict parameters of linear motion blur kernels, the length and orientation of the blur. We analyze the relationship between length and angle of linear motion blur that can be represented as digital filter kernels. A large dataset of blurred images is generated using a suite of blur kernels and used to train a regression CNN for prediction of length and angle of the motion blur. The coefficients of determination for estimation of length and angle are found to be greater than or equal to 0.89, even under the presence of significant additive Gaussian noise, up to a variance of 10% (SNR of 10 dB). Using our estimated kernel in a nonblind image deblurring method, the sum of squared differences error ratio demonstrates higher cumulative histogram values than comparison methods, with most test images yielding an error ratio of less than or equal to 1.25.
KEYWORDS: Image processing, Electronic imaging, Evolutionary algorithms, Video, Machine learning, Visual process modeling, Video processing, Super resolution, Data processing, Data modeling
Non-uniform motion blur, including effects commonly encountered in blur associated with atmospheric turbulence, can be estimated as a superposition of locally linear uniform blur kernels. Linear uniform blur kernels are modeled using two parameters, length and angle. In recent work, we have demonstrated the use of a regression-based Convolutional Neural Network (CNN) for robust blind estimation of the length and angle blur parameters of linear uniform blur kernels. In this work we extend the approach of regression-based CNNs to analyze patches in images and estimate the parameters of a locally-linear motion blur kernel, allowing us to model the blur field. We analyze the effectiveness of this patch-based approach versus patch size for two problems: synthetic images generated as a superposition of locally linear blurs, and synthetic images generated with a Zernike polynomial-based wavefront distortion applied at the pupil plane.
Facial classification has numerous real-world applications in various fields such as security and surveillance. However, images collected at long range through the atmosphere exhibit spatially and temporally varying blur and geometric distortion due to turbulence; consequently, making facial identification challenging. A multispectral facial classification approach is proposed utilizing machine learning for long-range imaging. A method for simulating turbulence effects is applied to a multispectral face image database to generate turbulence-degraded images. The performance of the machine learning method for this classification task is assessed to explore the effectiveness of multispectral imaging for improving classification accuracy over long ranges.
The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with ∼1 km focal length and with ∼50 milli-arcsecond angular resolution. VTXO is formed by using precision formation flying of two SmallSats: a smaller OpticsSat that houses the PFLs and navigation beacons while a larger DetectorSat contains an X-ray camera, a precision start tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly elliptical supersynchronous orbit allow the formation to hold in an inertial frame around the 90,000 km apogee for 10 hours of the 32.5 hour orbit with nearly a year mission lifetime. VTXO’s fine angular resolution enables measuring the environments close to the central engines of bright compact X-ray sources. This X-ray imaging capability allows for the study of the effects of dust scattering near to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure near to the compact object in X-ray novae such as Cyg X-1 and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The VTXO SmallSat and instrument designs, mission parameters, and science performance are described. VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions.
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