Aiming at the problems of low brightness, low contrast, noise and color distortion of images produced by existing true color night vision cameras, a neural network algorithm based on channel-calibrated convolution is proposed. The upper branch of the channel-calibrated convolution introduces a channel attention block to analyze the features between the RGB channels. This replaces the traditional convolution in the U-Net network, enabling color recovery and the retention of more image information. The Sobel loss function and color loss function are added to the traditional loss function to suppress noise, preserve image details, reduce chromatic aberration, and enhance contrast. An image dataset under real conditions is collected, which improves the processing effect of the actual data. The experimental results show that the algorithm in this paper can simultaneously deal with the brightness, contrast, noise and chromatic aberration of low-light images, and the enhancement effect is better than the existing mainstream algorithms. Compared with the traditional convolutional U-Net network, the novel method reduces the model complexity and improves operating speed, with a 13.71% reduction in computation, a 13.65% reduction in parameters, a 29.20% increase in PSNR, a 7.23% increase in SSIM, and a 10.46% decrease in chromatic aberration. The algorithm in this paper strikes a balance between imaging quality and speed.
