Large field-of-view optical imaging systems often face challenges in the presence of space-variant degradation. The existence of degradation leads to target detection and recognition being difficult or even unsuccessful. To address this issue, this paper proposes an adaptive anisotropic pixel-by-pixel space-variant correction method. First, we estimated region acquisition of local space-variant point spread functions (PSFs) based on Haar wavelet degradation degree distribution, and obtained initial PSF matrix estimation with inverse distance weighted spatial interpolation. Then, we established a pixel-by-pixel space-variant correction model based on the PSF matrix. Third, we imposed adaptive sparse regularization terms of the Haar wavelet based on the adaptive anisotropic iterative reweight strategy and non-negative regularization terms as the constraint in the pixel-by-pixel space-variant correction model. Finally, as the correction process is refined to each pixel, the split-Bregman multivariate separation solution algorithm was employed for the pixel-by-pixel spare-variant correction model to estimate the final PSF matrix and the gray value of each pixel. Through this algorithm, the "whole image correction" and "block correction" is avoided, the "pixel-by-pixel correction" is realized, and the final corrected images are obtained. Experimental results show that compared with the current advanced correction methods, the proposed approach in the space-variant wide field correction of a degraded image shows better performance in preserving the image details and texture information.