Several adaptive LMS L-filters, both constrained and unconstrained ones, are developed for noise suppression in images and being compared in this paper. First, the location-invariant LMS L-filter for a nonconstant signal corrupted by zero-mean additive white noise is derived. Subsequently, the normalized and the sign LMS L-filters are studied. It is shown that both these filters turn to be iden...

In this paper, the authors propose an efficient technique called Iterative and Adaptive Noise Detection Method (IANDM) for detecting the corrupted pixel in the image and for estimating the original value of the corrupted pixel. With the proposed method the noise pixel detection is improved by giving important to both the local and the global features. The proposed AMPMAD algorithm has a highest...

Graph neural networks (GNNs) have been extensively studied for prediction tasks on graphs. As pointed out by recent studies, most GNNs assume local homophily, i.e., strong similarities in neighborhoods. This assumption however limits the generalizability power of GNNs. To address this limitation, we propose a flexible GNN model, which is capable handling any graphs without being restricted thei...

This paper provides a literature review on Adaptive Line Enhancer (ALE) methods based on adaptive noise cancellation systems. Such methods have been used in various applications, including communication systems, biomedical engineering, and industrial applications. Developments in ALE in noise cancellation are reviewed, including the principles, adaptive algorithms, and recent modifications on t...