Learning to hash is a popular technique for fast similarity search on a large-scale image database. However, many hashing methods do not achieve satisfactory results because of the quantization loss in the straightforward binary code generation procedure. In order to address this problem, we propose a novel hash code reconstruction framework for existing unsupervised hashing methods. In our proposed approach, the hash codes are generated through reconstructing the original images with relaxed hamming vector representation, such that the final learned codes will be more approximate to characterize the intrinsic image structure. Moreover, our proposed hash code reconstruction algorithm is very efficient for computing, which can be generalized to various hashing methods. Extensive experiments are conducted on four public image datasets by incorporating our proposed scheme with different hashing methods, and the comparison results have shown that significant performance improvements can be achieved with minor additional time cost for fast similarity search task.