Deep learning can achieve the complex function approximation and the characteristics of
the input data by studying a deep nonlinear network. At present, one of the most important …

This letter proposes a sliced-gated-convolutional neural network with belief propagation
(SGCNN-BP) architecture for decoding long codes under correlated noise. The basic idea of …

Despite the recent upsurge of self‐supervised methods in single image denoising, achieving
robustness and efficiency of performance is still challenging due to some prevalent issues …

We propose a novel neural network-based adaptive image denoiser, dubbased as Neural
AIDE. Unlike other neural network-based denoisers, which typically apply supervised …

This letter proposes a novel neural network (NN) called GatedNet for decoding over-
impulsive noise channels. To reduce the impact of impulsive noise, the neurons in the …

This work proposes a memristor-based quantized convolutional auto-encoder (MQCAE) and
applies it in an image denoising application. The pulse width or amplitude is gradually tuned …

Error correction code is a major part of the physical communication layer, ensuring the
reliable transfer of data over noisy channels. Recently, neural decoders were shown to …

A method based on sparse denoising autoencoder for denoising hybrid noises in image is
proposed in this paper. The method is experimented on natural images and the performance …

Deep learning with multiple hidden layers denoising autoencoders (MHL-DAE) is commonly
used to denoise images and signals through dimension reduction. Here, we explore the …

Recently developed deep-learning-based denoisers often outperform state-of-the-art
conventional denoisers, such as the BM3D. They are typically trained to minimizethe mean …