Flir Systems, Incorporated

Nokia Corporation

Noiseless Imaging Oy Ltd

Vladimir Katkovnik

Alessandro Foi

Karen Egiazarian

Radu Ciprian Bilcu

Markku Kalevi Vehvilainen

Mejdi Trimeche

Enrique Sanchez-Monge

Sakari Tapio Alenius

Lucio Azzari

Dmitriy Paliy

Pavlo Molchanov

Enrique Sánchez-Monge

Various techniques are disclosed for reducing noise and enhancing sharpness of an input image. For example, a method includes performing an initial collaborative filtering and sharpening on the input image to generate a pilot image, using the pilot image to derive coefficients that are used to perform a second collaborative filtering on the input image to generate a filtered image. In some embodiments, the collaborative filtering and sharpening is performed using parameters that boost or enhance the differences in pixel values for the same spatial locations of the matched image blocks extracted during the collaborative filtering and sharpening process. Accordingly, the method according to various embodiments performs especially well for images that have weak spatial correlations among mutually similar blocks.

Systems and methods for efficient enhanced image filtering by collaborative sharpening in similarity domain

Various techniques are disclosed to suppress distortion in images (e.g., video or still images), such as distortion caused by atmospheric turbulence. For example, similar image blocks from a sequence of images may be identified and tracked along motion trajectories to construct spatiotemporal volumes. The motion trajectories are smoothed to estimate the true positions of the image blocks without random displacements/shifts due to the distortion, and the smoothed trajectories are used to aggregate the image blocks in their new estimated positions to reconstruct the sequence of images with the random displacements/shifts suppressed. Blurring that may remain within each image block of the spatiotemporal volumes may be suppressed by modifying the spatiotemporal volumes in a collaborative fashion. For example, a decorrelating transform may be applied to the spatiotemporal volumes to suppress the blurring in a transform domain, such as by alpha-rooting or other suitable operations on the coefficients of the spectral volumes.

Methods and systems for suppressing atmospheric turbulence in images

An apparatus, method, mobile station and computer program product are provided for filtering noise from a digital signal. In particular, a signal-dependent noise model is used that provides the pointwise (or pixelwise) standard deviation of the temporal noise of raw data outputted from a digital imaging sensor as a function of the image intensity. In addition, unlike conventional noise models, the standard deviation of the noise (&#x3c3;) is a parameterized function, where the parameters are key characteristics of the digital imaging sensor. These parameters may include, for example, the pedestal level (p), the quantum efficiency (q), and the analogue gain (&#x3b1;) associated with the digital imaging sensor.

Apparatus, method, mobile station and computer program product for noise estimation, modeling and filtering of a digital image

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Tampere, Finland

Vladimir Katkovnik