Greyscale based learning in BPNN for image restoration problem

A new method of back propagation learning with respect to the problem of image restoration which is named as greyscale based learning in back propagation neural networks (BPNN) is investigated. It is observed that by using this method the value of mean square error (MSE) decreases significantly. In addition, this method also gives good visual results when it is applied in image restoration problem. This method is also useful to tackle the inherited drawback of falling into local minima by reducing its effect on overall system by bifurcating the learning locally different for different grey scale values. The performance of this algorithm has been studied in detail with different combinations of weights. In short, this algorithm provides much better results especially when compared with the simple back propagation algorithm with any further enhancements and without going for hybrid solutions.