Method for reconstruction of pixel color values

Abstract

A method of color reconstruction includes a first process for a first pixel and a second process for the first pixel. The first process includes extracting a first kernel from a multi-color matrix, generating first variance weights from the first kernel, and generating a first color based on the first variance weights and adjacent pixel values of the first color. The second process includes extracting a second kernel from the multi-color matrix, generating second variance offsets from the second kernel, and generating a second color based on the second variance offsets and an adjacent pixel of the second color.

1 . A method for object-based color reconstruction in a multicolor matrix-based sensor arrangement comprising color sensors that have one first luminance component sensed at a relatively higher spatial frequency and two further chrominance components sensed at relatively lower spatial fancies, for a particular pixel not sensed in said first luminance component estimating its first color value through determining local gradients among various first luminance component values a said method being characterized by executing the following steps:
 and in accordance with such local gradients executing such estimating through along relatively stronger edge informations, interpolating with a relatively greater weight factor, in favor over interpolating along relatively weaker edge informations with a relatively lesser weight factor,   and for a particular pixel not sensed in a particular further chrominance component value estimating that further chrominance component's value in a direction along with relatively smaller differences evaluated in said first luminance component.   
   
   
       2 . A method as claimed in  claim 1 , wherein further chrominance component's values of said particular pixel are interpolated using neighboring pixels' information as based on whether they are situated within a same imaged object. 
   
   
       3 . A method as claimed in  claim 1  and applied to a Bayer matrix wherein said first luminance component is green. 
   
   
       4 . A method as claimed in Clam  2 , when said first luminance component's value is estimated on the basis of a 5×5 pixel kernel centered on said particular point. 
   
   
       5 . A met as claimed in  claim 1 , whilst through exponential gradient values adjusting an exponent value (k) for emphasizing edge clarity in a low-noise situation, or rather limiting noise propagation whilst still mitigating for color aliasing. 
   
   
       6 . A method as claimed in  claim 1 , whilst adding a low-pass filter step after estimating non-sampled colors for mitigating false-color spikes. 
   
   
       7 . A method as claimed in  claim 6 , whilst supplementing said low pass filter step with relatively enhancing spatial high frequencies relatively far from said low-pass filter's discriminatory frequency for edge sharpness enhancement  FIG. 5 ). 
   
   
       8 . A computer program comprising program instruction for controlling a computer to implement a method according to one of  claim 1 . 
   
   
       9 . A computer program product as being represented with a tangible read-only computer memory medium or being carried by an electrical signal, and comprising program instructions for controlling a cower to implement a method according to one of  claim 1 . 
   
   
       10 . An apparatus being arranged for implementing a method as claimed in  claim 1 . 
   
   
       11 . An apparatus according to  claim 10 , and executed as a filter facility for limiting noise propagation. 
   
   
       12 . An image facility comprising an image forming facility for forming an image on an as claimed in  claim 10 , and furthermore comprising a memory facility fed by said apparatus, processing means for dynamically interacting with pixel values in said memory and user output means for outputting a reconstructed user image.