ing at the problem of poor visual effect and low image quality of existing low-light images, a low-light image enhancement algorithm based on cascaded residual generative adversarial network is proposed. The algorithm uses constructed cascaded residual convolutional neural network as generator network and improved PatchGAN as discriminator network. First, training samples are synthesized through normal-light image on the basis of Retinex theory. Then, low-light images are converted from red-green-blue space to hue-saturation-value color space. Meanwhile, keeping hue and saturation unchanged, the value component is enhanced through the cascaded residual generator network. Besides, low-light image is enhanced through the way of discriminator network supervising generator network. They struggle against each other to promote the capability of generator network to enhance the low-light image. Experimental results show that the proposed enhancement algorithm obtains better visual effects and contrast in terms of synthetic low-light images and natural low-light images. Especially, for the synthetic low-light images, the proposed algorithm is obviously superior to other comparison algorithms in terms of peak signal-to-noise ratio and structural similarity.