Pixel based Texture in Painting using Optimal Neighborhood Positioning

Dermoscopy is an imaging technique used to facilitate accurate automatic identification and diagnosis of skin cancer. However, because of the presence of noise in an image in the form of hairs and bubbles, identification may lead to inaccurate results. An in painting algorithm was developed for reconstructing image backgrounds after the removal of hairs and bubbles. The algorithm assumes a Markov Random Field model for the surrounding texture and uses pixel-based texture synthesis techniques to determine the best value for an unknown target pixel, based on a neighborhood search model. The algorithm attempts to optimize the quality of in painting by choosing an optimal neighborhood location around the target pixel. Using mean–square error (MSE) metric, three neighborhood (window) sizes were objectively investigated for overall effectiveness of the algorithm. Larger window sizes (e.g., 5, and 7) provided output results with less visible artifacts. Thus, large window size provides better background reconstruction.