Image enhancement tasks have garnered significant attention in recent years, particularly regarding images captured in low-light conditions. Due to their inherent under-illumination characteristics, these images often suffer from challenges such as low contrast, low brightness, and noise that hinder their quality. With the recent advancements in deep learning, convolutional neural networks (CNNs) have demonstrated a high potential in addressing these challenges. In this paper, we propose a deep CNN-based approach that enhances color components a and b through Lab-space decomposition of images, utilizing a deep unsupervised dehazing network model as a baseline. Our approach considers both algorithmic and image decomposition, splitting the low-light image enhancement task into two parallel subtasks. Extensive experiments demonstrate that our proposed approach outperforms previous methods.
The massive multiple-input and multiple-output (MIMO) system based on channel state information (CSI) is the core technology of next-generation communication. As the complexity of the CSI matrix gradually increases, CSI feedback becomes more challenging. CSI feedback technology based on deep learning (DL) has been successful in frequency-division duplex (FDD) MIMO systems. In this paper, we propose a complex-valued lightweight neural network MADNet for CSI feedback. The network is based on an encoder-decoder structure, which enriches the information extraction of the CSI matrix by adopting channel information extraction modules and coordinates information extraction modules, which reduces the computational complexity of the network by using lightweight convolution. Experimental results show that our network outperforms most CNN-based network architectures at multiple compression rates and performs significantly better in both indoor and outdoor scenarios.
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