An Improved Fuzzified Frankot and Chellappa Method for Surface Normal Integration

We present an enhanced fuzzified variant of the classical Frankot-Chellappa algorithm for reconstructing surfaces from normal vector fields by using a multiplicative fuzzification process and an adaptive defuzzification strategy. By incorporating local spread metrics and noise-level estimation, the adaptive defuzzification process dynamically adjusts reconstruction weights, significantly improving robustness to noise. Experimental results demonstrate that the proposed method achieves superior accuracy compared to a previous fuzzy approach, particularly under noisy conditions.