Research On Face Image Enhancement Algorithms For Low-Light Scenes

Face images have always been a key data foundation in security and criminal investigations.However,due to lighting and shooting angles,faces captured in low-light scenes often suffer from low resolution and low luminance,making it difficult to identify facial features and unable to provide data support for related tasks.In this paper,we propose a small target low-light face image enhancement method and a face superresolution method based on spectral normalization for improving low brightness and low resolution of target face images.(1)For the problem that small target faces are difficult to recognize in low-light scenes,this paper proposes a small target face image light enhancement method for low-light scenes.Firstly,the multi-level feature extraction module is used to obtain rich face image features at different levels to dig out the face image information hidden in the dark as much as possible.Secondly,a self-attention mechanism is introduced in the luminance enhancement module to learn to capture pixel relationships at a distance in low-light face images and make the enhanced image more friendly.Finally,the feature fusion module integrates the luminanceenhanced feature maps and uses convolutional layers for fusion.Experiments prove that the proposed method can enhance the brightness of small target face images in low-light scenes more effectively compared with existing methods.(2)For small target luminance-enhanced face images with low resolution and missing details,this paper proposes a face super-resolution method based on spectral normalization to achieve a large upscaling factor of ultra-low resolution face images by a progressive face generation method.Firstly,a low-resolution image encoder is designed to fully extract the feature information of low-resolution face images with enhanced brightness.Secondly,we introduce a channel attention mechanism and design a series of noisy style blocks to reconstruct visually friendly and realistic high-quality faces.Finally,to improve the stability of model training while speeding up the model training,the spectral normalization,self-attentiveness mechanism,and two-time scale update rule are introduced in the discriminator.Experiments show that the proposed method yields superior results in terms of both subjective visual results and evaluation metrics compared with existing methods.