Zero-Learning Fast Medical Image Fusion

Clinical applications, such as image-guided surgery and noninvasive diagnosis, rely heavily on multi-modal images. Medical image fusion plays a central role by integrating information from multiple sources into a single, more understandable output. We propose a real-time image fusion method using pre trained neural networks to generate a single image containing features from multi-modal sources. The images are merged using a novel strategy based on deep feature maps extracted from a convolutional neural network. These feature maps are compared to generate fusion weights that drive the multi-modal image fusion process. Our method is not limited to the fusion of two images, it can be applied to any number of input sources. We validate the effectiveness of our proposed method on multiple medical fusion categories. The experimental results demonstrate that our technique achieves state-of-the-art performance in both visual quality, objective assessment, and runtime efficiency

Image Restoration using Plug-and-Play CNN MAP Denoisers

Plug-and-play denoisers can be used to perform generic image restoration tasks independent of the degradation type. These methods build on the fact that the Maximum a Posteriori (MAP) optimization can be solved using smaller sub-problems, including a MAP denoising optimization. We present the first end-to-end approach to MAP estimation for image denoising using deep neural networks. We show that our method is guaranteed to minimize the MAP denoising objective, which is then used in an optimization algorithm for generic image restoration. We provide theoretical analysis of our approach and show the quantitative performance of our method in several experiments. Our experimental results show that the proposed method can achieve 70x faster performance compared to the state-of-the-art, while maintaining the theoretical perspective of MAP

Media item relighting technique

The present invention concerns a method of method of relighting a media item comprising media elements. The method comprises, for at least some of the media elements: determining, in a first signal domain, a light transport function describing the appearance of a particular media element under different illumination conditions at least partly defined by positions of a light source used to illuminate the particular media element; sampling, in the first signal domain, the light transport function of the particular media element to obtain discrete data samples of the light transport function; projecting the data samples in the first signal domain into a sampling grid to obtain spatially sparsely and non-uniformly sampled light transport function; interpolating, in a second signal domain, the sparsely and non-uniformly sampled light transport function to obtain an approximate light transport matrix in the second signal domain; converting the approximate light transport matrix into the first signal domain to obtain an approximate substantially uniformly sampled light transport function in the first signal domain; and using, in the first signal domain, the approximate substantially uniformly light transport function to relight the media item