A MAP regularization super-resolution image reconstruction method based on improved immune algorithm

The method of processing computer signals based on super-resolution reconstruction eliminates the image resolution degradation caused by the objective lack of image formation system, thus formatting a higher spatial-resolution and clear image. Address on improvements of local searching ability of immune algorithm, ensuring diversity of initial solution and noncontinuous function optimization, the paper brought out a MAP regularization algorithm based on immune algorithm for super-resolution reconstruction. Corresponding mathematical model was also provided. Experiments were carried out on Lena image polluted by noise for regularization super-resolution reconstruction. Simulation comparison results show that MAP regularization reconstruction method based on immune algorithm and POCS are superior to nonuniform spatial samples interpolation method, while iteration number of former algorithms related to frame number of low-resolution image sequence.