Impulse noise is the main cause low image quality. The filtering of impulse noise has always been a research hotspot in the field of image processing. On the basis of theoretical analysis for current switching filtering algorithms in terms of detection time

detection accuracy

and recovery strategy

this study proposes a progressive iterative impulse noise detection algorithm

which can obtain a high recovery effect from noisy images. First

we adopt gray-level histograms that possess global statistical significance to identify the pixel gray value boundary of impulse noise and real pixels b

b. By using these histograms

we can distinguish the suspected points and real points. Second

we use the method of local structure significance to identify and classify the noise points from the suspected points. These points are then saved in Table G. Finally

according to the different noise types in Table G

we use 3 different strategies to remove the noise points. The experiments on three representative images with different noise densities and noise intensities show that our detection time is 520 times and 15 times faster than that of the two current classic algorithms

respectively. Furthermore

the proposed method has a detection accuracy of 99%

recover images with excellent visual effects

and enhances the peak signal-noise ratio to 12 dB. The proposed algorithm can protect the image detail and recover the original features of the image when filtering impulse noise. The proposed method can also compensate for the disadvantage of current switching filters in terms of detection time

detection accuracy

and peak signal-noise ratio.