The existing blur detection algorithm based on features has to calculate local features many times in order to optimize the blur response. Complicated adjacency relationship leads to large computing tasks and low efficiency. To solve the time problem, this paper proposes a local blur detection optimization algorithm with the perpendicular context in single scale. Firstly, we calculate features such as local heavy-tailedness feature, kurtosis feature, local power spectrum feature and local filters, Then naive Bayesian classifier is used to combine these features. Finally, this paper takes the blur response information of adjacent pixels as context to update the pixel itself, increases support region of context to take the information around the pixel into account more adequately, uses one-dimensional perpendicular context to reduce the amount of calculation, forms new energy function, and obtains the final blur response by minimizing the energy function. Experimental results show that the modified algorithm can detect local blur effectively and improve time efficiency.