No-Reference Low-Light Image Enhancement with Enhanced Feature Map

Since low-light images are of poor quality and carry noise, resulting in insufficient contrast and brightness, unclear details in the image, and the problem of high cost of obtaining paired low-light image datasets, a no-reference low-light image enhancement method for feature map enhancement is proposed under the influence of the Mach band effect of biological vision. Firstly, the model uses the enhanced filter block (EFB) to enhance the feature of images and feature maps, which can suppress noise and enhance the feature details to improve the ability of the network to learn features. Then, skip connection is combined with efficient spatial attention (ESA) module to extract global context information and local regional features by fusing enhanced shallow features and deep features, which effectively retains image color information, avoids detail loss, and improves the generalization ability of the network. Finally, the pixel estimation curve is used to adjust the dynamic range of the pixels in the low-light image and enhance the brightness of the low-light image. Experimental results show that the PSNR, SSIM, LPIPS and NIQE of the image processed by the algorithm reach 17.709 dB, 0.657, 0.239 and 3.486 respectively. Compared with the existing mainstream algorithms, it can better achieve the purpose of image enhancement, and effectively improves the image brightness and detail information, while maintaining the naturalness of the image.