Aiming at the denoising problem in image restoration, an adaptive weighted encoding and L_1/2 regularization method was proposed. Firstly, for many real images which have not only Gaussian noise, but have Laplace noise, an Improved L₁-L₂ Hybrid Error Model (IHEM) method was proposed, which could have the advantages of both L₁ norm and L₂ norm. Secondly, considering noise distribution change in the iteration process, an adaptive membership degree method was proposed, which could reduce iteration number and computational cost. An adaptive weighted encoding method was applied, which had a perfect effect on solving the noise heavy tail distribution problem. In addition, L_1/2 regularization method was proposed, which could get much sparse solution. The experimental results demonstrate that the proposed algorithm can lead to Peak Signal-to-Noise Ratio (PSNR) about 3.5 dB improvement and Structural SIMilarity (SSIM) about 0.02 improvement in average over the IHEM method, and it gets an ideal result to deal with the different noise.