A turbulent fuzzy target restoration algorithm with a nonconvex regularization constraint is proposed to address degradation issues, such as low signal-to-noise ratio, blurring, and geometric distortion, in target images caused by atmospheric turbulence and light scattering in long-range optoelectronic detection systems. First, we utilized latent low-rank spatial decomposition (LatLRSD) to obtain the target low-rank components, texture components, and high-frequency noise components. Next, two structural components were obtained by denoising the LatLRSD model; these were weighted and reconstructed in the wavelet transform domain, and nonconvex regularization constraints were added to the constructed target reconstruction function to improve the reconstruction blur and scale sensitivity problems caused by the traditional l_p norm (p=0,1,2) as a constraint term. The results of a target restoration experiment in long-distance turbulent imaging scenes show that compared with traditional algorithms, the proposed algorithm can effectively remove turbulent target blur and noise; the average signal-to-noise ratio of the restored target is improved by about 9 dB. Further, the proposed algorithm is suitable for multiframe or single-frame turbulent blur target restoration scenes.