Generalized cross validation (GCV) is a significant mean square error (MSE) estimator. It is widely used for image denoising because it can provide an optimal denoising threshold for these wavelet coefficients of noise image. However, the computational complexity of GCV is higher than that of the universal threshold denoising algorithm. In this study, an efficient and fast image denoising algorithm is proposed based on even step-length (ESL) GCV model. In ESL-GCV model, only the thresholds on even points are calculated from four to the maximum wavelet coefficient. In addition, the ESL-GCV model is optimized using the integer wavelet transform (IWT). These experimental results show that the IWT-based ESL-GCV model can provide lower computational complexity and the better peak signal-to-noise ratio (PSNR) than those of the traditional GCV. The proposed algorithm has important theoretical and practical value for image denoising in the future.