Objective

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Random-valued impulse noise (RVIN) is a common cause of image degradation that is frequently observed in images captured by digital camera sensors. In addition to degrading image quality

this type of noise also leads to pixel failure and inaccurate storage location or transmission. The presence of impulse noise may also introduce difficulties in feature extraction

target tracking

image classification

and subsequent image processing and analysis works. For RVIN

the noise value of a corrupted pixels uniformly distributed between 0 and 255. In this case

detecting the RVIN is very difficult. The available local image statistics for RVIN detection

which are used to determine whether the center pixel of an image patch is corrupted by RVIN noise or not

have are latively weak description ability

thereby restricting their accuracy to some extent and affecting the restoration performance of subsequent switching RVIN denoising modules.

Method

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Nine local image statistics

including eight neighbor rank-ordered logarithmic difference (ROLD) statistics and one min-imum orientation logarithmic difference (MOLD) statistics

were used to construct a highly sensitive RVIN noise-aware feature vector that can describe the RVIN likeness of the center pixel of a given patch. Based on this vector

RVIN noise-aware feature vectors extracted from numerous noisy patches

their corresponding noise labels were formed as a set of training pairs for a multi-layer perception (MLP) network

and the MLP-based RVIN detector was trained.

Result

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Comparative experiments were performed to test the estimation accuracy and denoising effect of the proposed RVIN detector. The proposed detector was compared with several state-of-the-art image denoising methods

including progressive switching median filter(PSMF)

ROLD-edge preserving regularization(ROLD-EPR)

adaptive switching median(ASWM)

robust outlyingness ratio nonlocal means(ROR-NLM)

MLP-edge preserving regularization(MLP-EPR)

convolutional neural network based(CNN-based)

blind convolutional neural network(BCNN)

and MLP neural network classifier(MLPNNC)

to demonstrate its estimation accuracy. Two image sets were used in the experiments. One image set included the "Lena"

"House"

"Peppers"

"Couple"

"Hill"

"Barbara"

"Boat"

"Man"

"Cameraman"

and "Monarch" images

whereas the other set contained 50 textured images that were randomly selected from the BSD database(unlike the noise detection model training set). For a fair comparison

all competing algorithms were implemented in the MATLAB 2017b environment on the same hardware platform. To verify the estimation accuracy of the proposed RVIN detector

we applied different RVIN noise ratios to images taken from commonly used image sets

applied the proposed detector to count the instances of error

false

and missed detections for a noisy image

and compared its performance with that of existing classical RVIN noise reduction algorithms. Usually

a higher rate of error detection indicates that more noise has been left undetected in an image

and a false detection can reduce the noise of normal non-distorted pixels during the noise reduction stage

which can lead to blurry images. The total number of errors represents the number of missed and false detections

whereas a smaller number of these detections corresponds to a lower algorithm detection error rate and a better image quality after noise reduction. Experimental results show that the proposed algorithm has a relatively balanced number of missed and false detections and ranks second among all compared algorithms in this respect

thereby offering a solid foundation for the subsequent noise reduction module. In the second image set

we combined the proposed RVIN detector with the generic iteratively reweighted annihilating filter(GIRAF) algorithm to form a RVIN noise reduction algorithm. To verify the effectiveness of the proposed detector

we applied different ratios of RVIN noise (i.e.

10%

20%

30%

40%

50%

and 60%) to 50 textured images and recorded the average peak signal-to-noise ratio (PSNR) of these images under each noise ratio. Experimental results show that the images restored by the proposed-GIRAF algorithm achieve the optimal PSNR under each noise ratio and that this algorithm greatly outperforms the Xu

Chen-GIRAF

and MLPNNC-GIRAF algorithms. The proposed-GIRAF algorithm also outperforms the second-best algorithm by 0.47 dB to 1.96 dB in terms of the average PSNR of its 50 images

thereby suggesting that the actual detection results of the proposed noise detector are the most effective for the subsequent noise reduction module. Experimental results also show that the proposed RVIN detector outperforms most of the existing detectors in terms of detection accuracy. As such

a switching RVIN removal method with an improved denoising performance can be obtained by combining the proposed RVIN detector with any inpainting algorithm.

Conclusion

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Extensive experiments show that the estimation accuracy of the proposed MLP-based noise detector is robust across a wide range of noise ratios. When combined with the GIRAF algorithm

this detector significantly outperforms the traditional RVIN denoising algorithm in terms of denoising effect.