High signal to noise ratio and high resolution have been the goal of remote sensing. Since the classical electromagnetic wave is influenced by the diffraction limit and quantum noise limit, increasing the resolution has been close to the limit of remote sensing, In this situation, in 14 years, the author through quantum remote sensing based theory, scientific experiment and the key technology research of the three phases, before the end of December 2014 completed the study of quantum remote sensing principle prototype.<p> </p> Quantum remote sensing prototype is based on the theory of quantum optics, which takes manipulation, preparation and control in quantum optical field as the experimental method. Through the experiment, the results obtained are the coherent light detection imaging resolution 2-3 times. Based on a large number of experimental studies, we completed the key technology of quantum remote sensing principle prototype, scheme design and principle prototype system. Through the test, the technical indicators of the principle prototype meet the requirements, which provide technical foundation for quantum remote sensing engineering principle prototype.
Considering that Double-density dual-tree(DD-DT)complex wavelet has translation invariance, anti-aliasing properties and more compact space intervals, based on the quantum-inspired parameter estimation, this paper proposed a new quantum-inspired noise reduction method based on DD-DT complex wavelet transform for remote sensing images, especially the SAR images. The general process is addressed as below: conduct a logarithmic transformation for the SAR images, convert the multiplicative speckle noises to additive noises; then decompose the DD-DT complex wavelets for each image, thus to get the wavelet coefficient for each layer in all detailed directions; consider the inter-scale correlation of wavelet coefficient, utilize the Bayesian estimation theory along with the quantum mechanics principle of superposition, calculate the estimated wavelet coefficient; and then process the data layer by layer, refactor the SAR images using the processed coefficients. Then conduct a anti-logarithmic transformation to get the noise reduction result. Compare with the results of traditional methods, the resulting images have a significant improvement in different evaluation functions such as the Peak Signal Noise Ratio, Edge Preserve Index etc. The results have also shown better noise reduction quality in the images.