:A light field camera can obtain 4D light field data from stereoscopic space and generate focal stacks with one shot. Depth information can be extracted by using a focus detection function. However

generalizing varying scenes is difficult because of the distinct response characteristics of focus detection functions. Furthermore

most of the existing methods lead to large defocusing errors

which are not robust in practical usage. In this paper

we present a new depth extraction method based on light field images (i.e.

focal slices and all-focus images) to obtain high-accuracy depth information. We develop a windowed focusing detection function based on gradient mean square error to extract depth information. Thereafter

we correct the defocusing errors by using the local search method for the area marked with the defocusing function. Finally

we synthesize the depth images to improve accuracy. The experiments on the Lytro dataset and our own data show that our approach achieves higher accuracy with less noise than other state-of-the-art methods. Precision increases by approximately 9.29%

and MSE decreases by approximately 0.056 compared with other advanced methods. The use of the windowed gradient mean square error function to extract depth information produces less speck noise. By using the color information from all-focus images

our approach can correct the defocusing error. Finally

the depth edges fused in an MRF framework are clear and maintain good consistency with the color image.The depth estimation by our approach is better than other methods for low texture images.