Denoising smooth‐detail representation of MRI‐SPECT fusion

Abstract

AbstractFusing of brain imaging data is a hot task that has been previously approached by preserving saliency information from inputs to the output enhanced fused image. In this article, we present a method for calculating fused image from an anatomical image and a functional image, in which detail saliency information is preserved while suppressing noise. Our method is based on smooth‐detail representation used as a decomposition model scheme and based on denoising fusion rules to predict a fused version of the desired output. Unlike our previous approach, the method proposed in this article presents a smooth‐detail model for greyscale image with the extension a single‐channel to a three‐channel. We also adopt denoising method for map to construct the estimated fused image. The effectiveness of our method is proved in terms of analyzing noisy image, making comparison between proposed method and deep learning methods, and computing complexity of running time, showing better performance on denoising and preserving luminance saliency information.