This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

Existing deep learning models for hyperspectral image (HSI) reconstruction achieve good
performance but require powerful hardwares with enormous memory and computational …

Abstract Model quantization is a crucial step for deploying super resolution (SR) networks on
mobile devices. However, existing works focus on quantization-aware training, which …

Despite breakthrough advances in image super-resolution (SR) with convolutional neural
networks (CNNs), SR has yet to enjoy ubiquitous applications due to the high computational …

Recently deep neural networks have achieved excellent performance on low-light raw video
enhancement. However they often come with high computational complexity and large …

As a core component in intelligent edge computing, deep neural networks (DNNs) will
increasingly play a critically important role in addressing the intelligence-related issues in …

Lighter and faster image restoration (IR) models are crucial for the deployment on resource-
limited devices. Binary neural network (BNN), one of the most promising model compression …

Since the resurgence of deep neural networks (DNNs), image super-resolution (SR) has
recently seen a huge progress in improving the quality of low resolution images, however at …

Light-weight super-resolution (SR) models have received considerable attention for their
serviceability in mobile devices. Many efforts employ network quantization to compress SR …

Remarkable achievements have been obtained with binary neural networks (BNN) in real-
time and energy-efficient single-image super-resolution (SISR) methods. However, existing …