Edge Sensing Interpolation Algorithm I

• From our description of non-adaptive algorithms, it can be seen that most of the color  interpolation is done by averaging neighboring pixels indiscriminately. This causes an artifact -- the "zipper effect" in the interpolated image. To combat with this artifact, it is natural to derive an algorithm that can detect local spatial features present in the pixel neighborhood and then makes effective choices as to which predictor to use that neighborhood. The result is a reduction or elimination of "zipper-type" artifacts. And algorithms that involve this kind of "intelligent" detection and decision process are referred as adaptive color interpolation algorithms.

• Human visual systems are sensitive to edges present in the images and non-adaptive color interpolation algorithms often fail around edges since they are not able to detect "edges". To deal with edges, let's first look at an  edge sensing algorithm[14][3][15].

• For convenience, figure 1 is duplicated here

• Interpolation of green pixels :

• First, define two gradients, one in horizontal direction, the other in vertical direction, for each blue/red position. For instance, consider B8 : define two gradients as , where | . | denotes absolute value
 
• Define some threshold value T
 
• The algorithm then can be described as follows : 
 
• The choice of T depends on the images and can have defferent optimum values from different neighborhoods. A particular choice of T is  [4]. In this case, the algorithm becomes :

• Interpolation of red/blue pixels : same as in Smooth hue transition interpolation in logarithmic exposure space. Other methods may be used as well.