A histogram is an effective form for extracting various types of features and has been attracting keen attention in pattern recognition fields. In visual recognition, however, the histogram features suffer from smoothing due to the processes of quantizing continuous input patterns into discrete codes and pooling them. Such smoothing degrades discriminative power by blurring the distinctive features. In this paper, we propose a novel method to get rid of such blurriness and reveal the essential discriminative information from the histogram features. We first model the blurring process based on the graph Laplacian of discrete codes and then formulate a method for deblurring histogram features in a computationally efficient form which works as post-processing just after feature extraction. The proposed method is also shown to be related to unsharp masking of an image restoration technique. In the experiments on image classification using BoW histogram features, the proposed deblurring method favorably improves performance of the histogram features.