In order to remove noise effectively, a new wavelet threshold denoising algorithm is constructed on the basis of traditional wavelet theory. First of all, select appropriate wavelet basis function and and decomposition level, and then appropriate threshold function and threshold value are selected for threshold processing. At last, the denoised image is derived by reassembling both the low-frequency and high-frequency components. The adjustment parameter is introduced into the threshold function to make it flexible in denoising. Through Matlab simulation, the evaluation index results of several denoising algorithms are compared. The experimental results show that the subjective visual effect and objective evaluation index of this algorithm is obviously better than other algorithms, and it has a certain level of practicality.
Conductivity-depth imaging (CDI) of data is generally applied in identifying conductive targets. CDI results will be affected by the bird attitude especially the pitch of the receiver coil due to the attitude, velocity of the aircraft and the wind speed. A CDI algorithm with consideration of pitch is developed based on two-component measurement. A table is established based on two-component B field response and the pitch is considered as a parameter in the table. Primary advantages of this method are immunity to pith errors and better resolution of conductive layers than results without consideration of pith. Not only the conductivity but also the pitch can be obtained from this algorithm. Tests on synthetic data demonstrate that the CDI results with pitch based on two-component measurement does a better job than the results without consideration of pitch and the pitch obtained is close to the true model in many circumstances.
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