The colour block registration method of fuzzy image under multi-layer P-spline geometric transformation

In order to overcome the problem that the traditional similarity measure is easy to be affected by the grey-scale migration, this paper proposes a colour block registration method of fuzzy image under the multi-layer P-spline geometric transformation. This method mainly sets sparse coding as similarity measure, and divides two images that are not registered into image blocks. K-singular value decomposition (K-SVD) algorithm is used to train image blocks, acquire analysis dictionary and find sparse coefficients. The multi-layer P-spline geometric transformation is used to simulate the fuzzy image, and the gradient descent method is used to optimise the objective function to complete the colour block registration of the fuzzy image under the multi-layer P-spline geometric transformation. The experimental results show that the registration time is the lowest, the root mean square error is kept below 0.04, and the registration accuracy is up to 100%.