Remove salt and pepper noise using deep learning method

Download (0)

Full text


Remove salt and pepper noise using deep learning method

Minal Mahajan


, Manjusha Borse





Year student,




LGNSCOE, Nashik.

1Student, E& TC, LGNSCOE, Nashik, Maharastra,India

2 Professor, E& TC, LGNSCOE, Nashik, Maharastra,India


Abstract: In therapeutic field, picture denoising is must for examination of pictures, conclusion and treatment of sicknesses. Presently multi day, picture denoising techniques dependent on profound learning are viable. We decide the nature of the denoised picture, crest sign to commotion proportion (PSNR), Mean Square Error (MSE) and contrast and preparing informational collection. A test result demonstrates that our methodology has preferable exhibition over some different procedures. In this paper, we present another picture denoising methods for expelling salt and paper clamor from defiled picture by utilizing profound learning.

Keyword :

- CNN DAE, Deep learning, Image denoising , Medical image ,MSE, PSNR, Salt & paper


Images in medical field, for example, MRI, Mammograms, CT and ultrasound are comprise different sorts and degrees of clamor, because of debasement in its obtaining and transmission by a wide range of impacts. The nearness of clamor in the therapeutic pictures has immediate or backhanded outcome that entangles the determination, examination and treatment process auspicious [1]. The nature of the first picture is influencing which produce poor choices either by people or machines in picture denoising process. A typical sort of picture clamor is the alleged salt and pepper commotion, which is dispersed all through the picture and comprises of just the most extreme or least power esteems (i.e.,0 or 255) in the dynamic range. For the most part, the evacuation of salt and pepper clamor comprises of two issues: (1) how to identify the loud pixels and (2) how to fix them. In this way, the point of clamor evacuation or decrease is improve the quality and precision in the medicinal picture however much as could be expected.

Analysts think about limitlessly on picture denoising which is a primary issue in the field of PC vision.

Most prominently examined and traditional therapeutic picture denoising strategies are change based, as discrete wavelet (DW) [2], Shearlet [5], curvelet [6], discrete cosine (DC) [7], isotropic dispersion sifting [8], two-sided channels [9].y is a loud picture created as a blend of picture x and some commotion v, all having picture units. The target of picture denoising is to recuperate a perfect picture x from a loud perception y which pursues a picture debasement model y = x + v. One normal supposition that will be that v is paper and salt clamor with standard deviation σ. Here we take σ equivalent to 0.01.

Huge information size utilizing profound learning strategies, it has been clarified that, profound design can offer aggressive outcome if the model has capacity to prepare with exceptionally enormous information measure. , which is an extreme issue with regards to restorative pictures, where clearly restricted datasets are accessible. Accordingly, notwithstanding looking for fitting denoising procedures, misusing the profound learning application to picture


handling issues (for instance denoising) with little dataset, similar to medicinal picture is as yet an open research zone.

The succession of the paper is sorted out as pursues. In segment II clarify related work. The proposed denoising model, design and some explorative ideas, are itemized in Sect.III, Sect. IV gives trial results. Finish of the paper clarify in segment V


2.1 Denoising Autoencoder :

It takes the crude information, goes it through a concealed layer and attempts to modify a similar contribution at the yield. Thus, on a very basic level it works like a solitary layer neural system where foreseeing the information just at the yield is favored as opposed to anticipating names. In this manner, the misfortune you register is between the crude information you have given and your anticipated contribution at the yield layer. Pretty much same information is the aftereffect of limiting this misfortune which is anticipated at the yield as the one you are giving.

An idle element from the crude highlights is considered by utilizing autoencoder while holding the capacity to make the crude contribution once again from the inactive highlights. Above all else, clamor is added to the crude information intentionally before giving it to the system. This is called as denoising. A few clamors are Gaussian and concealing commotion. However, it is important to remember that when misfortune is registered, it will be between the anticipated info and the first information as it were.

2.2 Deep Learning:

Profound learning is a type of AI calculations utilizes various layers to progressively separate higher level highlights from crude information dependent on fake neural systems. For instance, in picture handling, lower layers may indicate edges, while higher layer may decide human-important things, for example, digits/letters or faces.

In profound learning, a lot of information utilized which is for the most part acquired even in huge amounts, and that in this manner can supply a major number of "bits" of data for calculations to gain from. AI strategy are change towards development of numerous degrees of portrayal which is arranged as of late and moved by profound realizing which is improving methodology between the AI look into network. Learning of valuable portrayals of information are engaged in learning calculation for profound design, which are legitimate to the job needing to be done, and are masterminded in a chain of importance with different levels. There are numerous mental highlights for profound structures: Brain thought (as a profound design sorted out by a few zones of the mind); Cognitive proclamation and building explanation (arrange thoughts and ideas in a modernized manner and at different levels by people) Sharing of factual capacity for perform various tasks learning; Procedure unpredictability.

Learning can be directed, semi-administered or unaided .Many present day profound learning models depend on a fake neural system, by and large, Convolutional Neural Networks (CNN), despite the fact that they can likewise think propositional recipes or idle factors requested layer-wise in profound generative models, for example, the hubs in profound conviction systems and profound Boltzmann machines.


Our proposed medical image denoising model is shown in Fig. 1.

The involvement of our work can be summarized as follows:

1. We develop medical image denoising model using Deep Learning.

2. To reduce complication of diagnosis, analysis and treatment process timely.


3.1 Denoising Model:

Fig.3.1: Denoising Model

First Noisy picture provide for the encoder. An encoder is a gadget, circuit, transducer, programming system or calculation that changes over data starting with one organization or code then onto the next, for the point of standardization, speed or pressure. A blower enlists information (e.g., sound/video/pictures) into a more diminutive structure .Image pressure is the workmanship and study of decreasing the measure of information required to speak to a picture. Pressure can decrease the transmission time. Picture pressure framework is made out of 2 particular practical segments: an encoder and decoder. Encoder performs Compression while decoder performs Decompression.

Info picture f(x) is encouraged into the encoder, which makes a packed portrayal of information. At the point when a compacted picture is given to decoder, a remade yield picture f '(x) is produced. In still picture application, the encoded info and decoder yield are f(x, y) and f '(x, y). At long last, accomplish yield picture.

3.2 Deep learning for Image Denoising:

It is an auto encoder which has different layers aside from that it's preparation isn't same as a multi layered NN.

The procedure, to put it plainly, is done as pursues:

Fig. 3.2: Stacked denoising auto encoder


Offer racket to the data. It experiences the hid layer. Yield is delivered and adversity is resolved between the yield (which is the foreseen data) and the primary data. Continue until mix when the mishap is restricted. By then finally pass the full data through this framework and assemble the data present in the covered layer. This structures new data. Take this (accumulated) data and pass disturbance to it and seek after a comparable system starting there.

In the end after completed with the last layer, the data assembled in this last covered layer is by and by its structure new information. Original picture x is added with clamor to make boisterous picture x~. At that point it is goes through layer1 Encoder and Layer2 Encoder. After that Layer3 Decoder and Layer4 Decoder goes to get last yield denoise picture.

4. EXPERIMENTAL RESULTS 4.1 Training and Testing Data:

For salt and paper denoising with realized commotion level = 0.01.We pursue to utilize 150 pictures of size 512X512 for preparing informational index. Picture standardized into a size of (0, 1) since we use grayscale picture.

For shading picture use scale (0, 3). Out of 150 pictures in preparing information, we use (80% information) 120 pictures as preparing information and (20%data) 30 pictures for approval. Adam is utilized for improvement. We utilize 128 channels. Here bunch size is 20 and age is equivalent to 200.Loss are limited since we utilize twofold subsidiary. Take σ =0.01

In testing part, again utilize another 150 pictures of size 512 X 512 with standardized scale (0, 1).Give an uproarious picture from testing information and get a denoised picture streamlined to enter.

Fig.4.1 The paper & salt denoising results

Here we played out our proposed technique on prepared database. The presentation of proposed technique is discovered by Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR) .MSE and PSNR are the two blunder measurements used to think about picture pressure quality. The MSE speaks to the combined squared blunder between the compacted and the first picture, PSNR speaks to a proportion of the pinnacle mistake. The outcome shows picture before denoising and in the wake of denoising. For typical denoising σ =0.01 is appeared in underneath figure with when denoising.


a) Before denoising

b) After denoising

Fig.4.2 Image before and after denoising

The table1 shows various images randomly taken and their observed results before and after denoising with parameter as MSE and PSNR.


Table1: The average result of our medical image denoising model before & after denoising From above table shows PSNR is increased and MSE is decreased & give noise free image


Profound Learning is the regular strategy to channel salt-and pepper clamor assumes a significant job in medicinal picture preprocessing. It can improve the picture nature of recreation, and this is noteworthy practically speaking. In this paper, a profound learning was proposed for picture denoising to expel commotion from boisterous perception. The proposed technique demonstrates that in the wake of denoising with profound learning strategy MSE diminishes though PSNR expands which shows that the remaking is of higher quality.


1. Gao Q, Olgac N (2016) Determination of the bounds of imaginary spectra of LTI systems with multiple time delays. Automatica 72:235–241

2. Mondal T, Maitra M (2015) Denoising and compression of medical image in wavelet 2D. Int J Recent Innov Trends Comput Commun 6:173–178

3. Mustafa N, Li JP, Khan SA, Giess M (2015) Medical image de-noising schemes using wavelet transform with fixed form thresholding. Int J Adv Comput Sci Appl 6:173–178

4. Bahendwar YS, Sinh GR (2012) Efficient algorithm for denoising of medical images using discrete wavelet transforms. Math Methods Syst Sci Eng 142:158–162

5. Zhang X (2015) Image denoising using shearlet transform and nonlinear diffusion. Proc Sci 20:033016

6. Starck JL, Cands EJ, Donoho DL (2002) The curvelet transform for image denoising. IEEE Tran Image Process 11(6):670–684

7. Hu J, Pu Y, Wu X, Zhang Y, Zhou J (2012) Improved DCT-based nonlocal means filter for MR images denoising.

Comput Math Methods Med 2012:85–91

8. Sameh Arif A, Mansor S, Logeswaran R (2011) Combined bilateral and anisotropic-diffusion filters for medical image de-noising. IEEE Student Conf Res Dev SCOReD 2:420–424

Before Denoising After Denoising


1 223.65 24.65 57.21 30.55

2 226.54 24.56 44.99 31.59 3 214.68 24.81 56.5 30.6 4 217.07 24.78 36.62 32.49 5 219.08 24.74 42.62 31.83


9. Bhonsle D, Chandra V, Sinha GR (2012) Medical image denoising using bilateral filter. Int J Image Gr Signal Process 4:36–43

10. Dabov K, Foi A, Katkovnik V, Katkovnik V, Egiazarian K (2007) Image denoising by sparse 3-D transform- domain collaborative filtering. IEEE Trans Image Process 16:2080–209

11.S.Esakkirajan,T.Veerakumar,AdabalaN.Subramanyam,C.H.PremChand, Removal of high density salt and pepper noise through modified decision based unsymmetric trimmed median filter, IEEE Signal Process.Lett.18(5),(2011),287–290.




Related subjects :