Dual-channel contrast prior (Dual-CP) simulates contrast using the difference between the bright channel and the dark channel of an image, and it achieves good results in the blind restoration of blurred images. However, in practical applications, the values of the bright channel and the dark channel of an image are not distributed on 1 and 0 as theoretically researched. This paper proposes a blind image restoration algorithm that combines Dual-CP, L₀ regularization strength, and gradient prior, wherein an effective optimization algorithm is derived using semi-quadratic splitting method to solve the nonconvex L₀ minimization problem. Experiments demonstrate that the proposed method has better intuitive description recovery capabilities, and on the benchmark dataset presented by Levin et al., Köhler et al., and Lai et al., the average peak signal-to-noise ratio increased by 2.1051 dB, 1.1273 dB, and 0.4491 dB, respectively, and the average structural similarity increased by 0.1302, 0.0599, and 0.0158, respectively.