Adaptive Image Rendering Using a Nonlinear Mapping-Function-Based Retinex Model

Low-Light Image Enhancement Using Hybrid Deep-Learning and Mixed-Norm Loss Functions

This study introduces a low-light image enhancement method using a hybrid deep-learning network and mixed-norm loss functions, in which the network consists of a decomposition-net, illuminance enhance-net, and chroma-net. To consider the correlation between R, G, and B channels, YCbCr channels converted from the RGB channels are used for training and restoration processes. With the luminance, the decomposition-net aims to decouple the reflectance and illuminance and to train the reflectance, leading to a more accurate feature map with noise reduction. The illumination enhance-net connected to the decomposition-net is used to enhance the illumination such that the illuminance is improved with reduced halo artifacts. In addition, the chroma-net is independently used to reduce color distortion. Moreover, a mixed-norm loss function used in the training process of each network is described to increase the stability and remove blurring in the reconstructed image by reflecting the properties of reflectance, illuminance, and chroma. The experimental results demonstrate that the proposed method leads to promising subjective and objective improvements over state-of-the-art deep-learning methods.

Research on Guide Line Identification and Lateral Motion Control of AGV in Complex Environments

During actual operations, Automatic Guided Vehicles (AGV) will inevitably encounter the phenomena of overexposure or shadowy areas, and unclear or even damaged guide wires, which interfere with the identification of guide wires. Therefore, this paper aims to solve the shortcomings of existing technology at the software level. Firstly, a Fast Guide Filter (FGF) is adopted with the two-dimensional gamma function with variable parameters, and an image preprocessing algorithm in a complex illumination environment is designed to get rid of the interference of illumination. Secondly, an ant colony edge detection algorithm is proposed, and the guide wire is accurately extracted by secondary screening combined with the guide wire characteristics; A variable universe Fuzzy Sliding Mode Control (FSMC) algorithm is designed as a lateral motion control method to realize the accurate tracking of AGV. Finally, the experimental platform is used to comprehensively verify the series of algorithms designed in this paper. The experimental results show that the maximum deviation can be limited to 1.2 mm, and the variance of the deviation is less than 0.2688 mm2.