Images captured in low-light environments suffer from serious degradation due to insufficient light, leading to the performance decline of industrial and civilian devices. To address the problems of noise, chromatic aberration, and detail distortion for enhancing low-light images using existing enhancement methods, this paper proposes an integrated learning approach (LightingNet) for low-light image enhancement. The LightingNet consists of two core components: 1) the complementary learning sub-network and 2) the vision transformer (VIT) low-light enhancement sub-network. VIT low-light enhancement sub-network is designed to learn and fit the current data to provide local high-level features through a full-scale architecture, and the complementary learning sub-network is utilized to provide global fine-tuned features through learning transfer. Extensive experiments confirm the effectiveness of the proposed LightingNet.

Images and videos shot in low light are often accompanied by severe image degradation, such as color noise, chromatic aberrations and loss of details. Most existing convolutional neural network (CNN)-based low-light enhancement methods focus on decomposing the image into illumination and reflection parts via the Retinex model, but these methods often fail to adequately consider controlling noise during enhancement and perform poorly in the face of complex lighting environments. In this letter, we propose a powerful Vision Transformer-based Generative Adversarial Network (Transformer-GAN) for enhancing low-light images. Transformer-GAN consists of two subnets as follows: (1) the feature extraction is achieved by an iterative multi-branch network in the feature extraction subnet, and (2) the enhancement is completed in the image reconstruction subnet. The innovative core works are multi-head multi-covariance self-attention (MHMCA) and Light feature-forward module structures (LFFM) in Transformer-GAN. Experiments demonstrate that our method outperforms state-of-the-art low-light enhancement methods on popular low-light datasets.

Polyadenylation Poly(A) is an essential process during messenger RNA (mRNA) maturation in biological eukaryote systems. Identifying Poly(A) signals (PASs) from the genome level is the key to understanding the mechanism of translation regulation and mRNA metabolism. In this work, we propose a deep dual-dynamic context-aware Poly(A) signal prediction model, called multiscale convolution with self-attention networks (MCANet), to adaptively uncover the spatial–temporal contextual dependence information. Specifically, the model automatically learns and strengthens informative features from the temporalwise and the spatialwise dimension. The identity connectivity performs contextual feature maps of Poly(A) data by direct connections from previous layers to subsequent layers. Then, a fully parametric rectified linear unit (FP-RELU) with dual-dynamic coefficients is devised to make the training of the model easier and enhance the generalization ability. A cross-entropy loss (CL) function is designed to make the model focus on samples that are easy to misclassify. Experiments on different Poly(A) signals demonstrate the superior performance of the proposed MCANet, and an ablation study shows the effectiveness of the network design for the feature learning and prediction of Poly(A) signals.