Magnetic Resonance Imaging (MRI) reconstruction faces many challenges due to limited access to diverse training data, primarily driven by privacy concerns. This is where Federated Learning (FL) comes as a solution, enabling collaborative model training between several clients without direct data sharing. However, the varied data quality and computational capabilities of participating clients can significantly impact FL’s efficiency and performance in MRI reconstruction. In this paper, we introduce an adaptive client selection strategy for federated MRI reconstruction. Our method dynamically assesses client contributions, considering factors like data quality, diversity, and computational resources. This allows us to prioritize clients that offer the most beneficial updates to the global model. Through extensive experiments on various MRI datasets, we demonstrate that our adaptive approach significantly improves reconstruction quality and accelerates convergence compared to traditional FL. Moreover, our method consistently outperforms both random client selection and training with all available clients.

Light field technology represents a viable path for providing a high-quality VR content. However, such an imaging system generates a high amount of data leading to an urgent need for LF image compression solution. In this paper, we propose an efficient LF image coding scheme based on view synthesis. Instead of transmitting all the LF views, only some of them are coded and transmitted, while the remaining views are dropped. The transmitted views are coded using Versatile Video Coding (VVC) and used as reference views to synthesize the missing views at decoder side. The dropped views are generated using the efficient dual discriminator GAN model. The selection of reference/dropped views is performed using a rate distortion optimization based on the VVC temporal scalability. Experimental results show that the proposed method provides high coding performance and overcomes the state-of-the-art LF image compression solutions.

The increasing penetration of acquisition and display devices for Light Field (LF) content in the consumer market leads to the high proliferation of this new immersive media. This growing interest to LF images thus urgently raises the question of their compression. In this paper, we propose a convolutional neural networks (CNN)-based LF image coding scheme including both Rate Distortion Optimization (RDO) and post-processing steps. First, at the encoder side, the views are rearranged in sparse and dropped set of views. The former are compressed with a standard encoder and transmitted, while the dropped views are either linearly approximated or synthesized by a CNN using the encoded views as input. This choice is made on the basis of the proposed RDO process. At the decoder side, once the dropped views are either linearly approximated or synthesized by a CNN block, a post-processing step is performed to further enhance the quality of the reconstructed views. This post-processing block is based on superpixel to pixel-matching. Experimental results show that the proposed scheme provides views with high visual quality and overcomes the state-of-the-art LF image compression solutions by -30% in terms of BD-BR and 0.62 dB in BD-PSNR.

Computer vision applications such as refocusing, segmentation and classification become one of the most advanced imaging services. Light Field (LF) imaging systems provide a rich semantic information of the scene. Using a dense set of cameras and microlens arrays (Plenoptic camera), the direction of each ray coming from the scene toward the LF capture system can be extracted and represented by spatial and angular coordinates. However, such imaging system induces many drawbacks including the large amount of data produced and complexity increase for scene representation. In this paper, we propose an efficient LF image coding scheme. This scheme first encodes a sparse set of views using the latest hybrid video encoder (JEM). Then, it estimates a second sparse set of views using a linear approximation. At the decoder side, we use a Deep Learning (DL) approach to estimate the whole LF image from the reconstructed sparse sets of views. Experimental results show that the proposed scheme provides higher visual quality and overcomes the state of the art LF image compression solution by 30 % bitrate gain.