This paper proposes a noise removal algorithm to improve the quality of images captured by IR thermal cameras. Due to atmospheric disturbances and the temperature reflected from the surrounding objects, IR images are generally noisy. For removal of noise in IR thermal images the proposed algorithm has two stages. In the first stage, a support vector machine incorporated with fuzzy logic (FSVM) is used to obtain noise pixels from the thermal image and in the second stage an adaptive directional weighted median filter (ADWMF) is applied to restore noisy pixels. Our proposed algorithm is compared with another fuzzy- logic based noise classification technique, namely the adaptive fuzzy inference based directional median filter (AFIDMF). Simulation results indicate that the performance of the proposed filter is better, compared to AFIDMF based on the measures such as peak signal to noise ratio (PSNR) and structural similarity index (SSIM).
BaiX., ZhouF. and XueB., Infrared image enhancement through contrast enhancement by using multiscale new top-hat transform, Infrared Physics & Technology54 (2011), 61–69.
2.
YuanL.T., SweeS.K. and TsoC.P., Infrared image enhancement using adaptive trilateral contrast enhancement, Pattern Recognition Letters542015 (2015), 103–108.
3.
LiY., ZhangY., GengA., CaoL. and ChenJ., Infrared image enhancement based on atmospheric scattering model and histogram equalization, Optics & Laser Technology83 (2016), 99–107.
4.
CoelhoP.A., TapiaJ.E., PérezF., TorresS.N. and SaavedraC., Infrared light field imaging system free of fixed-pattern noise, Scientific Reports7 (2017), 13040. DOI: 10.1038/S41598-017-13595-7
5.
VollmerM. and MöllmannK.-P., Infrared Thermal Imaging Fundamentals, Research and Applications, WILEY-VCH Verlag GmbH & Co. KGaA, Boschstr. 122010, 69469 Weinheim, Germany.
6.
GonzalezR.C., Digital Image Processing, Pearson Education, India. (2009).
7.
LinT.C., A new adaptive center weighted median filter for suppressing impulsive noise in images, Inf. Sciences177(4) (2007), 1073–1087.
8.
SunT., GabboujM. and NeuvoY., Center weighted median filters: Some properties and their applications in image processing, Signal Processing35 (1994), 213–229.
9.
KafiehR. and RabbaniH., Wavelet-BasedMedical Infrared Image Noise Reduction Using Local Model for Signal and Noise, (2011). DOI: 10.1109/SSP.2011.5967756
10.
BudzanS. and WyżgolikR., Noise Reduction in Thermal Images, ICCVG 2014, LNCS8671 (2014), 116–123.
11.
BudzanS. and WyżgolikR., Remarks on Noise Removal in Infrared Images, Measurement Automation Monitoring61(06) (2015), 187–190.
12.
ValdesMaria del C., InamuraM., ValeraJ.D.R. and LuY., Multidimensional filtering approaches for pre-processing thermal images, Multidim Syst Sign Process17 (2006), 299–325. DOI 10.1007/s11045-006-0001-0
13.
GuptaK.K., BegM.R. and NiranjanJ.K., A Novel Approach to Fast Image Filtering Algorithm of Infrared Images based on Intro Sort Algorithm, IJCSI International Journal of Computer Science Issues8(6,1) (2011), 235–241.
14.
ZhouB., WangS., MaY., MeiX., LiB., LiH. and FanF., An IR image impulse noise suppression algorithm based on fuzzy logic, Infrared Physics & Technology60 (2013), 346–358.
15.
SchulteS., De WitteV. and KerreE.E., A fuzzy noise reduction method for color images, IEEE Trans Image Process16 (2007), 1425–1436.
16.
ToprakA. and GülerI., Impulse noise reduction in medical images with the use of switch mode fuzzy adaptive median filter, Digital Signal Process17 (2007), 711–723.
17.
MorillasS., GregoriV., Peris-FajarnésG. and SapenaA., Local self-adaptive fuzzy filter for impulsive noise removal in color images, Signal Process88 (2008), 390–398.
18.
YildirimM.T., BasturkA. and YukselM.E., Impulse noise removal from digital images by a detail-preserving filter based on type-2 fuzzy logic, IEEE Trans. Fuzzy Logic Fuzzy Syst16 (2008), 920–928.
19.
TohK.K.V. and IsaN.A.M., Cluster-Based Adaptive Fuzzy Switching Median Filter for Universal Impulse Noise Reduction, IEEE Transaction on Consumer Electronics56(4) (2010), 2560–2568.
20.
HussainA. and HabibM., A new cluster based adaptive fuzzy switching median filter for impulse noise removal, Multimed Tools Appl76 (2017), 22001–22018. DOI 10.1007/s11042-017-4757-z
21.
ThirilogasundariV., Suresh babuV. and Agatha JanetS., Fuzzy Based salt and pepper noise removal using adaptive switching median filter, Procedia Engineering38 (2012), 2858–2865.
22.
ToledoC.F.M., de OliveiraL., da SilvaR.D. and PedriniH., Image Denoising Based on Genetic Algorithm, IEEE Congress on Evolutionary Computation June Cancún México, (2013), 20–23.
23.
SinghV., DevR., DharN.K., AgrawalP. and VermaN.K., Adaptive Type-2 Fuzzy Approach for Filtering Salt and Pepper Noise in Grayscale Images, IEEE Transactions On fuzzy Systems26(5) (2018), 3170–3176.
24.
KhanS. and LeeD.-H., An adaptive dynamically weighted median filter for impulse noise removal, EURASIP Journal on Advances in Signal Processing 201767 (2017), 1–14.
25.
HabibM., HussainA., RasheedS. and AliM., Adaptive fuzzy inference system based directional median filter for impulse noise removal, Int. J. Electron. Commun. (AEÜ)70 (2016), 689–697.
26.
DongY. and XuS., A New Directional Weighted Median Filter for Removal of Random-Valued Impulse Noise, IEEE Signal Processing Letters14(3) (2007), 193–196.
27.
KangC.C. and WangW.J., Fuzzy reasoning-based directional median filter design, Signal Process89 (2009), 344–351.
28.
HabibM., HussainA. and ChoiT.S., Adaptive threshold based fuzzy directional filter using background information, Appl Soft Comput29 (2015), 471–478.
29.
CortesC. and VapnikV., Support-Vector Networks, Machine Learning20 (1995), 273–297.
30.
LinT.-C., A Novel Decision-Based Median -Type Filter using SVM for Image Denoising, International Journal of Innovative Computing, Information and Control8 5(A) (2012), 3189–3202.
31.
RoyA., SinghaJ., DeviS.S. and LaskarR.H., Impulse noise removal using SVM classification based fuzzy filter from gray scale images, Signal Processing128 (2016), 262–273.
32.
RoyA. and SinghaJ., Removal of Impulse Noise from Gray Images using Fuzzy SVM Based Histogram Fuzzy Filter, Journal of Circuits, Systems, and Computers, World Scientific Publishing Company27(9) (2018), 1850139 (23 pages). DOI: 10.1142/S0218126618501396
33.
RoyA., SinghaJ. and LaskarR.H., Impulse Noise Removal from Color Images: An Approach using SVM Classification Based Fuzzy Filter Proc. of the 2017 IEEE Region 10 Conference (TENCON), Malaysia, (November 5–8, 2017) (2017), 929–934.
34.
RoyA. and LaskarR.H., Fuzzy SVM based fuzzy adaptive filter for denoising impulse noise from color images, Multimed Tools Appl (2019), doi:10.1007/s11042-018-6303-z.
35.
LinC.-F. and WangS.-D., Fuzzy Support Vector Machines, IEEE Transactions on Neural Networks13(2) (2002), 464–471.
36.
SomanK.P., LoganathanR. and AjayV., Machine Learning with SVM and Other Kernel Methods, PHI Learning Private Limited (2011).
37.
KimB.H., KimM.Y. and ChaeY.S., Background Registration-Based Adaptive Noise Filtering of LWIR/MWIR Imaging Sensors for UAV Applications, Sensors18(60) (2018), doi: 10.3390/s18010060
38.
CockburnW., Common Errors in Medical Thermal Imaging, Proceedings of Information7 (2006), 165–177.
39.
HuangX., MdS., IslamR., LiaoM. and LiS., High Quality Infrared Images with Novel Algorithm for Multi-Noises Removal, Proceedings of the World Congress on EngineeringIII (July 3–5, 2013), London, U.K. 2013.
