Abstract: In an example embodiment a method, apparatus and computer program product are provided. The method includes grouping pixels of an image into a plurality of pixel groups. The method includes determining representative descriptors for the plurality of pixel groups and further grouping the plurality of pixel groups into a set of pixel groups based at least on similarity between the representative descriptors for the plurality of pixel groups. The method also includes receiving a selection of at least one region of the image. The method includes segmenting at least one object of the image based at least on an overlap between the at least one region and the set of pixel groups, wherein the at least one object is associated with one or more pixel groups of the set of pixel groups that overlap with the at least one region.

Abstract: An apparatus for capturing two different types of light simultaneously is described. A filter array is used for filtering light arriving at an image sensor. The filter array comprises per pixel filters arranged in rows, so if a row comprises filters, they are all of the same type. Different exposure settings may be used for rows having different filter configurations.

Abstract: An apparatus for capturing two different types of light simultaneously is described. A filter array is used for filtering light arriving at an image sensor. The filter array comprises per pixel filters arranged in rows, so if a row comprises filters, they are all of the same type. Different exposure settings may be used for rows having different filter configurations.

Abstract: In an example embodiment a method, apparatus and computer program product are provided. The method includes grouping pixels of an image into a plurality of pixel groups. The method includes determining representative descriptors for the plurality of pixel groups and further grouping the plurality of pixel groups into a set of pixel groups based at least on similarity between the representative descriptors for the plurality of pixel groups. The method also includes receiving a selection of at least one region of the image. The method includes segmenting at least one object of the image based at least on an overlap between the at least one region and the set of pixel groups, wherein the at least one object is associated with one or more pixel groups of the set of pixel groups that overlap with the at least one region.

Abstract: A method and related apparatuses, the method comprising: setting up a network of a plurality of mobile devices (208); obtaining sensory data from at least one mobile device of the network (210); analyzing the obtained sensory data in order to detect at least one predetermined event (212); and in response to detecting at least one predetermined event (214), providing an indication about the detected event to at least one mobile terminal of the network.

Abstract: A method comprising: receiving a plurality of images corresponding to a time period covering the intended moment for releasing the shutter; receiving an audio signal associated with the plurality of images using audio capturing means; analyzing the received audio signal in order to determine an auditory event associated with a desired output image; and selecting at least one of the plurality of images on the basis of the analysis of the received audio signal for further processing in order to obtain the desired output image.

Abstract: An apparatus and a method is provided. An apparatus including at least one color separation diffraction grating configured to direct different spectral components of incident light in different directions; one or more further diffraction gratings configured to at least partially compensate for dispersion in one or more of the different spectral components of light; and one or more image sensors configured to detect the one or more dispersion compensated spectral components of light.

Abstract: A method and related apparatuses, the method comprising: setting up a network of a plurality of mobile devices (208); obtaining sensory data from at least one mobile device of the network (210); analysing the obtained sensory data in order to detect at least one predetermined event (212); and in response to detecting at least one predetermined event (214), providing an indication about the detected event to at least one mobile terminal of the network.

Abstract: The invention relates to a method, a device, an image quality evaluation module and a computer program product for determining the quality of an image. In an input image, at least one color component is iteratively restored with a deblur parameter, which deblur parameter is increased at each iteration. The iteration is stopped when an overshooting in the final image exceeds a predetermined value. After the iteration has been stopped, a number of iterations is defined and the quality of the image is determined according to the number of iterations.

Abstract: A method comprising: receiving a plurality of images corresponding to a time period covering the intended moment for releasing the shutter; receiving an audio signal associated with the plurality of images using audio capturing means; analyzing the received audio signal in order to determine an auditory event associated with a desired output image; and selecting at least one of the plurality of images on the basis of the analysis of the received audio signal for further processing in order to obtain the desired output image.

Abstract: Methods and systems are provided for adjusting the contrast and intensity of digital images. Digital images may be processed by first creating a histogram that identifies the intensity of components of the digital image. A transformation function is then computed using the histogram. The transformation function can be used to adjust the intensity of a low light portion of the digital image and is applied to individual color components of the digital image. Finally, the contrast of a portion of the digital image other than the low light portion of the digital image is adjusted.

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, a device, an image quality evaluation module and a computer program product for determining the quality of an image. In an input image, at least one color component is iteratively restored with a deblur parameter, which deblur parameter is increased at each iteration. The iteration is stopped when an overshooting in the final image exceeds a predetermined value. After the iteration has been stopped, a number of iterations is defined and the quality of the image is determined according to the number of iterations.

Abstract: Methods and systems are provided for adjusting the contrast and intensity of digital images. Digital images may be processed by first creating a histogram that identifies the intensity of components of the digital image. A transformation function is then computed using the histogram. The transformation function can be used to adjust the intensity of a low light portion of the digital image and is applied to individual color components of the digital image. Finally, the contrast of a portion of the digital image other than the low light portion of the digital image is adjusted.

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 present invention relates to an image pre-processing method for a pattern detection system, such as a bar code detection system. The method comprises the steps of: detecting a start point and an end point of a pattern on the basis of image data of at least a portion of a captured image, estimating a point spread function from the image data or a modified image data on the basis of the detected start point and the end point, and restoring the image data or the modified image data using the estimated point spread function.

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.

Abstract: The present invention relates to an image pre-processing method for a pattern detection system, such as a bar code detection system. The method comprises the steps of: detecting a start point and an end point of a pattern on the basis of image data of at least a portion of a captured image, estimating a point spread function from the image data or a modified image data on the basis of the detected start point and the end point, and restoring the image data or the modified image data using the estimated point spread function.