Improved DWT for Multi-Focus Image Fusion

Image fusion plays an important role in day-to-day applications. An image is corrupted by noise blurring and can have the poor visual quality. Image fusion is used to enhance the quality of a degraded image. It is one of the important task and pre-processing step in digital image processing. With the rapid development of the medical image technology, medical image fusion becomes increasingly important in medical analysis and diagnosis. Digital Image fusion may be categorized into two broad domains which are Spatial Domain and Transform Domain. The main techniques for image fusion are average method, principle component analysis, Brovey transform, artificial neural networks, pyramid method, wavelet transform for gray-scale images. Earlier proposed method suffers from the noise, artifacts and spectral degradation. The average method leads to the undesirable side effects such as reduced contrast. The weighted wavelet-based method for fusion of PET and CT images has been proposed, However, this method confronted with the problem of selecting the parameters of weight, that is to say this method depended on the weights given by the user. Therefore, different weights will lead to different fused results. Pyramid methods used for image fusion suffers from blocking artifacts and creates undesired edges. Single wavelet also give no proper results