Morphological and geometric edge filters for edge enhancement in depth images

A method comprising:

• detecting an input digital image of an object, the digital image comprising data pixels
• contaminated by noise and confidence pixels corresponding to the data pixels;
• computing a morphological filter by matching the confidence pixels in the input digital image with a set of matching templates, and using a set of masking templates to determine the data pixels and confidence pixels in the filtered image;
• computing an edge filter by performing computation of distances between the data pixels along a plurality of directions to determine an edge direction, and determining the data pixels and the confidence pixels in a filtered image based on the edge direction;
• and
• applying at least one of the morphological filter and the edge filter to filter the digital image,
• wherein the morphological and edge filters are applied by running a sliding window on the input digital image and computing a filter output for each position of the sliding window, wherein the sliding window represents a block of the data pixels and the confidence pixels extracted from the input digital image for each position, wherein the confidence pixel values provide accuracy of an estimation of corresponding data pixels,
• wherein computing the morphological filter comprises measuring a distance between a window of the confidence pixels and the set of matching templates to determine a best matching template, determining an output confidence value corresponding to a window location based on a center pixel of a best matching template of the set of matching templates, and determining an output data value corresponding to the window location using a masking template corresponding to the best matching template applied to the data values in the window,
• wherein the distance between the window of the confidence pixels and the matching template is a sum of absolute differences or a sum of squared differences between corresponding confidence pixel values in the window and the matching template, and
• wherein the confidence pixel values and the masking template pixel values are binary, the distance between the confidence pixels and the matching template is the Hamming distance, and computing the aggregate comprises computing an AND binary function of the corresponding confidence pixel values and the masking template pixel values, and outputting a median value of the data pixel values at positions where an output of the AND binary function is equal to 1.

At least one non-transitory machine-readable medium comprising a plurality of instructions,

• executed on a computing device, to facilitate the computing device to perform operations comprising:
• detecting an input digital image of an object, the digital image comprising data pixels
• contaminated by noise and confidence pixels corresponding to the data pixels;
• computing a morphological filter by matching the confidence pixels in the input digital image with a set of matching templates, and using a set of masking templates to determine the data pixels and confidence pixels in the filtered image;
• computing an edge filter by performing computation of distances between the data pixels along a plurality of directions to determine an edge direction, and determining the data pixels and the confidence pixels in a filtered image based on the edge direction;
• and
• applying at least one of the morphological filter and the edge filter to filter the digital image,
• wherein the morphological and edge filters are applied by running a sliding window on the input digital image and computing a filter output for each position of the sliding window, wherein the sliding window represents a block of the data pixels and the confidence pixels extracted from the input digital image for each position, wherein the confidence pixel values provide accuracy of an estimation of corresponding data pixels,
• wherein computing the morphological filter comprises measuring a distance between a window of the confidence pixels and the set of matching templates to determine a best matching template, determining an output confidence value corresponding to a window location based on a center pixel of a best matching template of the set of matching templates, and determining an output data value corresponding to the window location using a masking template corresponding to the best matching template applied to the data values in the window,
• wherein the distance between the window of the confidence pixels and the matching template is a sum of absolute differences or a sum of squared differences between corresponding confidence pixel values in the window and the matching template, and
• wherein the confidence pixel values and the masking template pixel values are binary, the distance between the confidence pixels and the matching template is the Hamming distance, and computing the aggregate comprises computing an AND binary function of the corresponding confidence pixel values and the masking template pixel values, and outputting a median value of the data pixel values at positions where an output of the AND binary function is equal to 1.