Abstract: Various techniques are disclosed for systems and methods to provide image resolution enhancement. For example, a method includes: receiving an original image (e.g., a visible light image) of a scene comprising image pixels identified by pixel coordinates; resizing the original image to a larger size, where the resized image is divided into a first plurality of reference blocks; enhancing a resolution of the resized image by iteratively: injecting high frequency data into the resized image, extracting from the resized image a first plurality of matching blocks that meet a mutual similarity condition with respect to the reference block, and adjusting the high frequency data of the reference block based on a correlation between the reference block and the first plurality of matching blocks. A system configured to perform such a method is also disclosed.

Abstract: 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.

Abstract: 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.

Abstract: Various techniques are disclosed for systems and methods to provide image resolution enhancement. For example, a method includes: receiving an original image (e.g., a visible light image) of a scene comprising image pixels identified by pixel coordinates; resizing the original image to a larger size, where the resized image is divided into a first plurality of reference blocks; enhancing a resolution of the resized image by iteratively: injecting high frequency data into the resized image, extracting from the resized image a first plurality of matching blocks that meet a mutual similarity condition with respect to the reference block, and adjusting the high frequency data of the reference block based on a correlation between the reference block and the first plurality of matching blocks. A system configured to perform such a method is also disclosed.

Abstract: 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 (?) 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 (?) associated with the digital imaging sensor.

Abstract: 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 (?) 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 (?) associated with the digital imaging sensor.

Abstract: The invention relates to a method for filtering. In the method an adaptive filtering is performed to an input signal. The filtered signal is further interpolated. The input signal is also interpolated for adapting the adaptive filtering. In the method the properties of the interpolation of the filtered signal are adaptable. The invention also relates to an apparatus comprising an adaptive filter for filtering an input signal, a first interpolator for interpolating the filtered signal, and a second interpolator for interpolating the input signal. The interpolated input signal is used to adapt the adaptive filter. The apparatus further comprises a first adapting block for adapting the properties of the first interpolator.