EG-RetinexNet: entropy-guided illumination enhancement using Retinex theory and Swish-based CNN

LCCNet: luminance–chroma collaborative cross-attention network for low-light image enhancement

Results indicate that the proposed luminance–chroma collaborative design effectively improves reconstruction fidelity and structural preservation under the evaluated low-light conditions.

WREN: Low Light Image Enhancement Using Retinex theory-based Double U-Net-like Structures

WREN is an LLIE neural network with double U-Net-like structures trained end-to-end with a scale-invariant loss function, which gives robustness against the illumination scaling, and achieves the state-of-the-art performance across multiple datasets.

IA2former: Illumination-Aware Attention-based Transformer for Low-light Image Enhancement

Results validate the effectiveness and robustness of the proposed illumination-aware modeling strategy for low-light image enhancement, IA2former, which effectively captures long-range dependencies, improves detail restoration, and preserves spatial structures under challenging illumination conditions.

Low-light image enhancement technology based on vision transformer

This work validates the design effectiveness of decoupling global and local representations within a frozen backbone, and establishes a new baseline for parameter-efficient enhancement.

Optimization of Methods for Restoring Motion-Coded Blurred Images Based on Deep Residual Networks

Motion-coded blurred image restoration is an important research topic in computer vision and intelligent imaging systems, directly affecting the accuracy of scene perception and information extraction. To address the limitations of existing methods in complex motion blur kernel estimation, deep network optimization, and multi-channel information fusion, an optimized restoration framework based on deep residual networks is proposed. The method incorporates a multi-scale residual learning architecture, enhanced skip-connection mechanisms, and a channel attention module to improve blur feature representation, training efficiency, and color information utilization. Experimental results demonstrate that the proposed approach achieves superior restoration performance, reaching a PSNR of 32.15 dB and an SSIM of 0.949, while significantly improving pattern recognition accuracy in high-speed production scenarios. The multi-scale framework effectively enhances adaptability to spatially varying motion blur, and the channel attention mechanism improves color fidelity and visual quality. Beyond industrial inspection applications, the proposed method is applicable to image reconstruction and information recovery tasks in intelligent sensing systems, including optical-electromagnetic imaging, remote sensing observation, and antenna-assisted imaging platforms, where motion-induced degradation can reduce the reliability of feature extraction and target interpretation. The study provides an effective engineering solution for high-quality image restoration and robust visual information recovery under dynamic imaging conditions.

Prior-calibrated spatial-frequency Retinex decomposition for unsupervised low-light image enhancement