To overcome the problems that captured image contains a considerable noise and affects the processing result, and beetle swarm optimization (BSO) algorithm is easy to fall into the local optimal solution in texture image processing, an improved sine cosine strategy based beetle swarm optimization (SCBSO) algorithm is proposed and applied to low-illuminance texture image enhancement. First, a logistic model is introduced to increase the diversity of the initial solution group. Then, combined with the SCBSO, the search strategy of the algorithm is improved and time-varying acceleration factor is added to realize the automatic updating of the parameters, thereby improving the convergence speed and search accuracy. Finally, the improved SCBSO algorithm is combined with the chromosome structure to achieve an accurate search for the optimal grayscale distribution of the image. In a standard function test, the SCBSO algorithm shortens the performance time by 16.56% and 14.78% compared to the original algorithm under two categories of functions. The image contrast is enhanced and the natural characteristics are better preserved. As compared with the comparison algorithm, the lightness order error (LOE) of the SCBSO algorithm is reduced by 37.8%, the visual information fidelity (VIF) is increased by 15.3%, and the peak signal-to-noise ratio (PSNR) is increased by 12.9%. The textural features of the image are well preserved during denoising.