Integrating Kalman filter noise residue into U-Net for robust image denoising: the KU-Net model.

In low-level image processing, where the main goal is to reconstruct a clean image from a noise-corrupted version, image denoising continues to be a critical challenge. Although recent developments have led to the introduction of complex architectures to improve denoising performance, these models frequently have more parameters and higher computational demands. Here, we propose a new, simplified architecture called KU-Net, which is intended to achieve better denoising performance while requiring less complexity. KU-Net is an extension of the basic U-Net architecture that incorporates gradient information and noise residue from a Kalman filter. The network's ability to learn is improved by this deliberate incorporation, which also helps it better preserve minute details in the denoised images. Without using Image augmentation, the proposed model is trained on a limited dataset to show its resilience in restricted training settings. Three essential inputs are processed by the architecture: gradient estimations, the predicted noisy image, and the original noisy grey image. These inputs work together to steer the U-Net's encoding and decoding stages to generate high-quality denoised outputs. According to our experimental results, KU-Net performs better than traditional models, as demonstrated by its superiority on common metrics like the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). KU-Net notably attains a PSNR of 26.60 dB at a noise level of 50, highlighting its efficacy and potential for more widespread use in image denoising.

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality.

Augmented Reality (AR) is in high demand in medical applications. The aim of the paper is to provide automatic surgery using AR for the Transcatheter Aortic Valve Replacement (TAVR). TAVR is the alternate medical procedure for open-heart surgery. TAVR replaces the injured valve with the new one using a catheter. In the existing model, remote guidance is given, while the surgery is not automated based on AR. In this article, we deployed a spatially aligned camera that is connected to a motor for the automation of image capture in the surgical environment. The camera tracks the 2D high-resolution image of the patient's heart along with the catheter testbed. These captured images are uploaded using the mobile app to a remote surgeon who is a cardiology expert. This image is utilized for the 3D reconstruction from 2D image tracking. This is viewed in a HoloLens like an emulator in a laptop. The surgeon can remotely inspect the 3D reconstructed images with additional transformation features such as rotation and scaling. These transformation features are enabled through hand gestures. The surgeon's guidance is transmitted to the surgical environment to automate the process in real-time scenarios. The catheter testbed in the surgical field is controlled by the hand gesture guidance of the remote surgeon. The developed prototype model demonstrates the effectiveness of remote surgical guidance through AR.

A comparative analysis of masking empirical mode decomposition and a neural network with feed-forward and back propagation along with masking empirical mode decomposition to improve the classification performance for a reliable brain-computer interface.

In general, extraction and classification are used in various fields like image processing, pattern recognition, signal processing, and so on. Extracting effective characteristics from raw electroencephalogram (EEG) signals is a crucial role of the brain-computer interface for motor imagery. Recently, there has been a great deal of focus on motor imagery in the EEG signals since they encode a person's intent to do an action. Researchers have been using MI signals to assist paralyzed people and even move them on their own with certain equipment, like wheelchairs. As a result, proper decoding is an important step required for the interconnection of the brain and the computer. EEG decoding is a challenging process because of poor SNR, complexity, and other reasons. However, choosing an appropriate method to extract the features to improve the performance of motor imagery recognition is still a research hotspot. To extract the features of the EEG signal in the classification task, this paper proposes a Masking Empirical Mode Decomposition (MEMD) based Feed Forward Back Propagation Neural Network (MEMD-FFBPNN). The dataset consists of EEG signals which are first normalized using the minimax method and given as input to the MEMD to extract the features and then given to the FFBPNN to classify the tasks. The accuracy of the proposed method MEMD-FFBPNN has been measured using the confusion matrix, mean square error and which has been recorded up to 99.9%. Thus, the proposed method gives better accuracy than the other conventional methods.