A method for color image restoration based on the concept of Markov Random Fields and space-filling curves is presented. This work is a vectorial extension of a scalar deterministic solution for Markov Random Fields (MRFs). The proposed method represents an efficient alternative to the use of the vectorial deterministic solution for MRFs. The application of space filling curve transformation allows one to apply the MRF algorithm to a scalar image with N3 grey levels (typically N = 256). The scalar MRF approach is based on expressing the energy function by means of the Euclidean norm in the vectorial space. This approach implies a high computational load. The new method involves a computational load lower than the vectorial case because the energy function is presented in the scalar space obtained after space filling curve based transformation.
Markovian approach to color image restoration based on space filling curves
REGAZZONI, CARLO;
1997-01-01
Abstract
A method for color image restoration based on the concept of Markov Random Fields and space-filling curves is presented. This work is a vectorial extension of a scalar deterministic solution for Markov Random Fields (MRFs). The proposed method represents an efficient alternative to the use of the vectorial deterministic solution for MRFs. The application of space filling curve transformation allows one to apply the MRF algorithm to a scalar image with N3 grey levels (typically N = 256). The scalar MRF approach is based on expressing the energy function by means of the Euclidean norm in the vectorial space. This approach implies a high computational load. The new method involves a computational load lower than the vectorial case because the energy function is presented in the scalar space obtained after space filling curve based transformation.
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