Abstract: The present disclosure relates to techniques for fusing deep learning-based image reconstructions with noisy image measurements with provable assurances that the resulting improved image does not remove the information content of the original noisy measurements or image. Particularly, aspects are directed to obtaining measurement data from an imaging modality, generating a base image by solving an optimization problem using at least a signal model and the measurement data, generating, using a deep-learning model, a predicted image based on the measurement data, selecting a modified operator based on the signal model, generating an enhanced image by solving the modified optimization problem using at least: (i) the base image or the measurement data, (ii) the signal model, (iii) the predicted image, and (iv) the modified operator, and outputting the enhanced image.

Abstract: Improved colorimetric analysis of liquid samples is provided. A sample holder is used that delivers predetermined volumes of sample individually to each of several colorimetric test patches at the same time with a sliding action. An opaque housing is employed to prevent ambient light from reaching the test patches when color images of the test patches are acquired. Preferably, a mobile electronic device including a camera is attached to the opaque housing to acquire the images. Optical microscopy can be performed in addition to the colorimetric analysis.

Abstract: Selected scene regions are imaged. IMAGING CHANNEL: mirrors (preferably MEMS) address an imaging sensor to regions. CALIBRATION CHANNEL: the mirrors direct radiation from a source to a calibration sensor, along an imaging-channel segment. Beam splitter(s) let the channels share optical path at the mirrors. To minimize imaging-channel diffractive blur, the calibration channel modifies wavefront angle and smoothness at the mirrors—measuring (and setting mirrors to optimize) PSF sharpness, then applying these measurements (and settings) to optimize imaging-channel settings by iterative multidimensional gradient search. An afocal lens receives scene radiation, magnifying deflection at the scene. An FOR is imaged on the imaging sensor; the mirrors address the sensor to a narrow FOV within the FOR; the lens enlarges deflections to cover the FOR. Plural diffraction-grating orders communicate between calibration source and sensor when the selected region is in plural scene portions, regardless which FOV is addressed.

Abstract: Selected scene regions are imaged. IMAGING CHANNEL: mirrors (preferably MEMS) address an imaging sensor to regions. CALIBRATION CHANNEL: the mirrors direct radiation from a source to a calibration sensor, along an imaging-channel segment. Beam splitter(s) let the channels share optical path at the mirrors. To minimize imaging-channel diffractive blur, the calibration channel modifies wavefront angle and smoothness at the mirrors—measuring (and setting mirrors to optimize) PSF sharpness, then applying these measurements (and settings) to optimize imaging-channel settings by iterative multidimensional gradient search. An afocal lens receives scene radiation, magnifying deflection at the scene. An FOR is imaged on the imaging sensor; the mirrors address the sensor to a narrow FOV within the FOR; the lens enlarges deflections to cover the FOR. Plural diffraction-grating orders communicate between calibration source and sensor when the selected region is in plural scene portions, regardless which FOV is addressed.

Abstract: Selected scene regions are imaged. IMAGING CHANNEL: mirrors (preferably MEMS) address an imaging sensor to regions. CALIBRATION CHANNEL: the mirrors direct radiation from a source to a calibration sensor, along an imaging-channel segment. Beam splitter(s) let the channels share optical path at the mirrors. To minimize imaging-channel diffractive blur, the calibration channel modifies wavefront angle and smoothness at the mirrors—measuring (and setting mirrors to optimize) PSF sharpness, then applying these measurements (and settings) to optimize imaging-channel settings by iterative multidimensional gradient search. An afocal lens receives scene radiation, magnifying deflection at the scene. An FOR is imaged on the imaging sensor; the mirrors address the sensor to a narrow FOV within the FOR; the lens enlarges deflections to cover the FOR. Plural diffraction-grating orders communicate between calibration source and sensor when the selected region is in plural scene portions, regardless which FOV is addressed.