Low-light image enhancement based on multilevel feature fusion

Low-light images are often challenged by significant noise, lack of contrast, low brightness, and color distortion. Accurate portrayal of local details and reasonable calibration of global information are crucial for image enhancement: the former facilitates texture restoration, and the latter guarantees accurate brightness. Based on this, this paper proposes a new lowlight image enhancement algorithm combining local-global convolutional neural network and Laplace pyramid decomposition. The algorithm focuses on local detail enhancement and global luminance distribution under low-light conditions, extracts and supplements local details through simultaneous multi-scale top-down and bottom-up feature learning, and then realizes global recalibration of channel weights with the help of global merging and projection operations. The Laplace pyramid is further used to decompose the image into low-frequency and high-frequency components, enhance the high-frequency details and use the low frequency global information for calibration and optimization, and ultimately merge the high and low-frequency information to achieve fine enhancement. Test results on LOL and four other unpaired datasets show that the LG-LPDNet proposed in this paper outperforms the state-of-the-art algorithms both subjectively and objectively, and is able to effectively restore normal brightness images with vivid colors and clear textures.