To address the challenges posed by image blurring in indoor visual localization, a study on the localization of blurred images was conducted by proposing an indoor visual localization system based on blurry images. Firstly, the blurriness level of images was assessed using a blurriness model, and then the images were deblurred using an improved image restoration model to obtain clear images. Secondly, the performance of image retrieval was enhanced using a deep learning-based image retrieval model to improve the accuracy of the localization system. Furthermore, a visual localization algorithm based on the essential matrix, adaptable to multiple features, was employed in conjunction with the SuperPoint + SuperGlue feature extraction algorithm to enhance the system's robustness to changes in illumination and viewpoint, and to improve processing efficiency. The local feature transformers(LoFTR) algorithm was integrated to address scenarios with no or weak texture. The system extracted features and matching information from pairs of original and retrieval images, and estimated the essential matrix using the 5-point method and the random sample consensus algorithm. Finally, the camera's absolute pose of the query image was obtained. Experimental results demonstrate that the system achieves precise localization results, with an average median localization error of 0.067 m and a camera angle error of 1.826°. It can be seen that the proposed method effectively addresses the challenges of image blurring in indoor visual localization, significantly improving localization accuracy.