Aiming at solving the problem of prior constraints on variational bayesian super-resolution reconstruction method, we propose a novel prior model to overcome the under-constraint of non-edge regions of image due to total variation prior, so the generation and spread of noise are further suppressed. We combine the weighted total variation model and L1 norm model, achieving a variational bayesian super-resolution reconstruction method based dual sparse priors. The super-resolution results of the simulation data and real data demonstrate that our algorithm is more effective and stable than the same type of other methods.
We have designed the super-solution technology for low light level imaging in the field of remote sensing. Low light level image super-resolution is realized with a method of super-resolution processing based on worked example learning and the information of high-resolution visible image. Multi-frame image and single-frame image of the super-resolution algorithms are combined to apply in the low light level imaging in the field of remote sensing. With the ground targets as the reference, the results illustrate that the higher resolution images are obtained and a number of image data are satisfactory. This technology has impressive potential for improving the efficiency of low light level remote sensing.
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