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IR and visible image fusion using DWT and bilateral filter

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Abstract

Image fusion is a process to combine or fuse various images captured from different sensors, different viewpoints, multi-focused, multi-exposure, etc. The fused image is capable of providing the complementary information captured from different images. Infrared and visible image fusion has become extensively employed in both military and civilian applications because to incredible breakthroughs in sensor technology. In this paper, a novel approach (DWTBF) of an infrared (IR) and visible (VI) image fusion based on Discrete Wavelet Transform (DWT), and a bilateral filter is proposed. DWT produces higher quality fusion image than the spatial domain, and it is used to decompose the image into a series of coefficients, falling in different frequency bands. A bilateral filter (BF), which is an edge-preserving and smoothing filter is used to obtain fusion weights. The “low-frequency” and “high-frequency” sub-bands generated using DWT are treated in a dissimilar manner and an averaging and weighted averaging based fusion strategy is proposed respectively. The final image is reproduced using inverse discrete wavelength transform (IDWT). The typical Discrete Wavelet Transform- bilateral filter (DWTBF) based fusion delivers better results and preserves more details of visible image and clearer infrared objects at the same time. The final fused image shows prominent results in terms of \(N^{{{\raise0.7ex\hbox{${AB}$} \!\mathord{\left/ {\vphantom {{AB} F}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$F$}}}}\), \(SSIM\), \(SCD\), and \(Q^{{{\raise0.7ex\hbox{${AB}$} \!\mathord{\left/ {\vphantom {{AB} F}}\right.\kern-\nulldelimiterspace} \!\lower0.7ex\hbox{$F$}}}}\) and it outperforms as compared to similar available existing techniques.

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References

  • Alexander T (2014) TNO image fusion dataset. https://figshare.com/articles/TNO_Image_Fusion_Dataset/1008029

  • AlFayez F, El-Soud MWA, Gaber T (2020) Thermogram breast cancer detection: a comparative study of two machine learning techniques. Appl Sci 10(2):551

    Article  Google Scholar 

  • Alseelawi N, Hazim HT, ALRikabi HTS (2022) A novel method of multimodal medical image fusion based on hybrid approach of NSCT and DTCWT. Int J Online Biomed Eng 18(3):114–133

    Article  Google Scholar 

  • Azarbad M, Ebrahimzade A, Izadian V (2011) Segmentation of infrared images and objectives detection using maximum entropy method based on the Bee algorithm. Int J Comput Inf Syst Ind Manag Appl 3:26–33

    Google Scholar 

  • Bavirisetti DP, Dhuli R (2016) Fusion of infrared and visible sensor images based on anisotropic diffusion and Karhunen-Loeve transform. IEEE Sens J 16(1):203–209

    Article  Google Scholar 

  • Benedetto JJ, Konstantinidis I, Rangaswamy M (2009) Phase-coded waveforms and their design: The role of the ambiguity function. IEEE Signal Process Mag 26(1):22–31

    Article  Google Scholar 

  • Bhandari AK, Kumar IV (2019) A context sensitive energy thresholding based 3D Otsu function for image segmentation using human learning optimization. Appl Soft Comput J 82:105570

    Article  Google Scholar 

  • Bhatnagar G, Wu QMJ (2011) An image fusion framework based on human visual system in framelet domain. Int J Wavelets Multiresolution Inf Process 10(1):1250002

    Article  MathSciNet  Google Scholar 

  • Bruce AM (2001) A review of digital watermarking. Dep Eng Univ Aberdeen 2(5):63–65

    Google Scholar 

  • Chandra BS (2015) Digital camera image fusion algorithm using Laplacian pyramid. Int J Comput Sci Mob Comput 4(7):43–49

    Google Scholar 

  • Chen J, Li X, Wu K (2022) Infrared and visible image fusion based on relative total variation decomposition. Infrared Phys Technol 123:104112

    Article  Google Scholar 

  • Chen Y, Cheng L, Wu H, Mo F, Chen Z (2022) Infrared and visible image fusion based on iterative differential thermal information filter. Opt Lasers Eng 148:106776

    Article  Google Scholar 

  • Cvejic N, Lewis J, Bull D, Canagarajah N (2006) Region-based multimodal image fusion using ICA bases. Proc Int Conf Image Process ICIP 7(5):1801–1804

    Google Scholar 

  • Daniel E, Anitha J, Kamaleshwaran KK, Rani I (2017) Optimum spectrum mask based medical image fusion using Gray Wolf Optimization. Biomed Signal Process Control 34:36–43

    Article  Google Scholar 

  • Eynard D, Kovnatsky A, Bronstein MM (2014) Laplacian colormaps: a framework for structure-preserving color transformations. Comput Graph Forum 33(2):215–224

    Article  Google Scholar 

  • Gautam R, Datar S (2017) Application of image fusion techniques on medical images. Int J Curr Eng Technol 7(1):161–167

    Google Scholar 

  • Goyal S, Wahla R (2015) A review on image fusion. In: 2019 Int Conf Commun Signal Process vol. 4, no. 2, pp 7582–7588

  • Gupta J, Pathak S, Kumar G (2022) A hybrid optimization-tuned deep convolutional neural network for bare skinned image classification in websites. Multimed Tools Appl 2022:1–23

    Google Scholar 

  • Habeeb NJ, Omran SH, Radih DA (2018) Contrast enhancement for visible-infrared image using image fusion and sharpen filters. In: ICOASE 2018 - Int Conf Adv Sci Eng, pp 64–69

  • Han X, Zhang Ll, Du Ly, Huan Kw, Shi Xg (2015) Fusion of infrared and visible images based on discrete wavelet transform. Proc SPIE 9795:387–392. https://doi.org/10.1117/12.2216054

    Article  Google Scholar 

  • Han D, Li L, Guo X, Ma J (2022) Multi-exposure image fusion via deep perceptual enhancement. Inf Fusion 79:248–262

    Article  Google Scholar 

  • Hassanien AE, Mahdy LN, Ezzat KA, Elmousalami HH et al (2020) Automatic x-ray covid-19 lung image classification system based on multi-level thresholding and support vector machine. medRxiv. https://doi.org/10.1101/2020.03.30.20047787v1

    Article  Google Scholar 

  • Hermessi H, Mourali O, Zagrouba E (2021) Multimodal medical image fusion review: theoretical background and recent advances. Signal Process 183:108036

    Article  Google Scholar 

  • Hong R, Cao W, Pang J, Jiang J (2014) Directional projection based image fusion quality metric. Inf Sci (NY) 281:611–619

    Article  MathSciNet  Google Scholar 

  • Indira KP, Hemamalini RR (2015) Evaluation of choose max and contrast based fusion rule using DWT for PET, CT images. Indian J Sci Technol https://doi.org/10.17485/ijst/2015/v8i16/74556

  • Kaur H, Koundal D, Kadyan V (2021) Image fusion techniques: a survey. Arch Comput Methods Eng 28(7):4425–4447

    Article  Google Scholar 

  • Kaur R, Singh S (2017) An artificial neural network based approach to calculate BER in CDMA for multiuser detection using MEM. In: Proc. 2016 2nd Int. Conf. Next Gener. Comput. Technol. NGCT 2016, no. October, pp 450–455

  • Krishnamoorthy S, Soman KP (2010) Implementation and comparative study of image fusion algorithms. Int J Comput Appl 9(2):25–35

    Google Scholar 

  • Kumar KPK, Geethakumari G (2014) Mean-variance blind noise estimation for CT images. Adv Intell Syst Comput 264:417–428

    Google Scholar 

  • Li H, Wu XJ (2018) Infrared and visible image fusion using latent low-rank representation. arxiv:1804.08992

  • Liang H, Jia H, Xing Z, Ma J, Peng X (2019) Modified grasshopper algorithm-based multilevel thresholding for color image segmentation. IEEE Access 7:11258–11295

    Article  Google Scholar 

  • Liu Z, Feng Y, Zhang Y, Li X (2016a) A fusion algorithm for infrared and visible images based on RDU-PCNN and ICA-bases in NSST domain. Infrared Phys Technol 79:183–190

    Article  Google Scholar 

  • Liu Y, Chen X, Ward RK, Wang J (2016b) Image fusion with convolutional sparse representation. IEEE Signal Process Lett 23(12):1882–1886

    Article  Google Scholar 

  • Liu CH, Qi Y, Ding WR (2017) Infrared and visible image fusion method based on saliency detection in sparse domain. Infrared Phys Technol 83:94–102

    Article  Google Scholar 

  • Lu T, Li S, Fang L, Jia X, Benediktsson JA (2017) From Subpixel to superpixel: a novel fusion framework for hyperspectral image classification. IEEE Trans Geosci Remote Sens 55(8):4398–4411

    Article  Google Scholar 

  • Meher B, Agrawal S, Panda R, Abraham A (2019) A survey on region based image fusion methods. Inf Fusion 48:119–132

    Article  Google Scholar 

  • Mehta T, Mehendale N (2021) Classification of X-ray images into COVID-19, pneumonia, and TB using cGAN and fine-tuned deep transfer learning models. Res Biomed Eng 37(4):803–813

    Article  Google Scholar 

  • Mitianoudis N, Stathaki T (2007) Pixel-based and region-based image fusion schemes using ICA bases. Inf. Fusion 8(2):131–142

    Article  MATH  Google Scholar 

  • Nemade V, Pathak S, Dubey AK, Barhate D (2022) A review and computational analysis of breast cancer using different machine learning techniques. Int J Emerg Technol Adv Eng 12(3):111–118

    Article  Google Scholar 

  • Nomura S, Yamanaka K, Katai O, Kawakami H (2004) A new method for degraded color image binarization based on adaptive lightning on grayscale versions. IEICE Trans Inf Syst E87-D(4):1012–1020

    Google Scholar 

  • Ochoa H, Rao KR (2003) A hybrid DWT-SVD image-coding system (HDWTSVD) for monochromatic images. IEEE 5022:1056–1066

    Google Scholar 

  • Panguluri SK, Mohan L (2020) Discrete wavelet transform based image fusion using unsharp masking. Period Polytech Electr Eng Comput Sci 64(2):211–220

    Article  Google Scholar 

  • Patil U, Mudengudi U (2011) IEEE 2011 IEEE International Conference on Image Information Processing (ICIIP) - Shimla, Himachal Pradesh, India (2011.11.3-2.pdf), no. Iciip

  • Petrović VS, Xydeas CS (2003) Sensor noise effects on signal-level image fusion performance. Inf Fusion 4(3):167–183

    Article  Google Scholar 

  • Petrović VS, Xydeas CS (2004) Gradient-based multiresolution image fusion. IEEE Trans Image Process 13(2):228–237

    Article  MATH  Google Scholar 

  • Petschnigg G, Agrawala M, Hoppe H, Szeliski R, Cohen M, Toyama K (2004) Digital photography with flash and no-flash image pairs. In: ACM SIGGRAPH 2004 Papers, SIGGRAPH 2004, pp 664–672

  • Pohl C, Van Genderen JL (1998) Review article Multisensor image fusion in remote sensing: concepts, methods and applications. Int J Remote Sens 19(5):823–854

    Article  Google Scholar 

  • Qiu C, Wang Y, Zhang H, Xia S (2017) Image fusion of CT and MR with sparse representation in NSST domain. Comput Math Methods Med 2017:1–13

    Article  MathSciNet  MATH  Google Scholar 

  • Ren L, Pan Z, Cao J, Liao J, Wang Y (2021) Infrared and visible image fusion based on weighted variance guided filter and image contrast enhancement. Infrared Phys Technol 114:103662

    Article  Google Scholar 

  • Sasikala M, Kumaravel N (2007) A comparative analysis of feature based image fusion methods. Inf Technol J 6(8):1224–1230

    Article  Google Scholar 

  • Shah P, Merchant SN, Desai UB (2010) Fusion of surveillance images in infrared and visible band using curvelet, wavelet and wavelet packet transform. Int J Wavelets Multiresolution Inf Process 8(2):271–292

    Article  MathSciNet  MATH  Google Scholar 

  • Shao L, Yan R, Li X, Liu Y (2014) From heuristic optimization to dictionary learning: a review and comprehensive comparison of image denoising algorithms. IEEE Trans Cybern 44(7):1001–1013

    Article  Google Scholar 

  • Silva SM, Jung CR (2020) Real-time license plate detection and recognition using deep convolutional neural networks. J vis Commun Image Represent 71:55–62

    Article  Google Scholar 

  • Simone G, Farina A, Morabito FC, Serpico SB, Bruzzone L (2002) Image fusion techniques for remote sensing applications. Inf Fusion 3(1):3–15

    Article  Google Scholar 

  • Singh R, Khare A (2014) Fusion of multimodal medical images using Daubechies complex wavelet transform - a multiresolution approach. Inf Fusion 19(1):49–60

    Article  Google Scholar 

  • Singh R, Srivastava R, Prakash O, Khare A (2012) Multimodal medical image fusion in dual tree complex wavelet transform domain using maximum and average fusion rules. J Med Imaging Heal Informatics 2(2):168–173

    Article  Google Scholar 

  • Singh H, Kumar V, Bhooshan S (2013) Anisotropic diffusion for details enhancement in multiexposure image fusion. ISRN Signal Process 2013:1–18

    Article  Google Scholar 

  • Singh R, Singh S, Sharma N (2019) The hybrid approach for image watermarking using GLCM algorithm. Int J Recent Technol Eng 8:105–111

    Google Scholar 

  • Singh S, Mittal N, Singh H (2020a) A multilevel thresholding algorithm using LebTLBO for image segmentation. Neural Comput Appl 32(21):16681–16706

    Article  Google Scholar 

  • Singh S, Mittal N, Singh H (2020b) Multifocus image fusion based on multiresolution pyramid and bilateral filter. IETE J Res. https://doi.org/10.1080/03772063.2019.1711205

    Article  Google Scholar 

  • Singh S, Mittal N, Singh H (2020c) Classification of various image fusion algorithms and their performance evaluation metrics. Comput Intell Mach Learn Healthcare Info. https://doi.org/10.1515/9783110648195-009

    Article  Google Scholar 

  • Singh S, Mittal N, Singh H (2021) Review of various image fusion algorithms and image fusion performance metric. Arch Comput Methods Eng 28(5):3645–3659

    Article  Google Scholar 

  • Singh S, Mittal N, Singh H (2022) A feature level image fusion for IR and visible image using mNMRA based segmentation. Neural Comput Appl 34(10):8137–8154

    Article  Google Scholar 

  • Sreeja P, Hariharan S (2018) An improved feature based image fusion technique for enhancement of liver lesions. Biocybern Biomed Eng 38(3):611–623

    Article  Google Scholar 

  • Tao J, Li S, Yang B (2010) Multimodal image fusion algorithm using dual-tree complex wavelet transform and particle swarm optimization. Commun Comput Inf Sci 93:296–303

    MATH  Google Scholar 

  • Tian J, Chen L (2010) Multi-focus image fusion using wavelet-domain statistics. In: Proc. - Int Conf Image Process ICIP, pp 1205–1208

  • Tomasi C, Manduchi R (1998) Bilateral filtering for gray and color images. In: Proceedings of the IEEE International Conference on Computer Vision, pp 839–846

  • Wang X, Bai S, Li Z, Sui Y, Tao J (2021) The PAN and MS image fusion algorithm based on adaptive guided filtering and gradient information regulation. Inf Sci (NY) 545:381–402

    Article  MathSciNet  Google Scholar 

  • Wang J, Ke C, Wu M, Liu M, Zeng C (2021) Infrared and visible image fusion based on Laplacian pyramid and generative adversarial network. KSII Trans Internet Inf Syst 15(5):1761–1777

    Google Scholar 

  • Wongsawat Y, Ochoa H, Rao KR (2004) A modified hybrid DCT-SVD image-coding system. In: 2004 IEEE Reg. 10 Conf. TENCON 2004., vol. A, pp 335–338

  • Yang B, Li S (2010) Multifocus image fusion and restoration with sparse representation. IEEE Trans Instrum Meas 59(4):884–892

    Article  Google Scholar 

  • Yeh CH, Lin CH, Lin MH, Kang LW, Huang CH, Chen MJ (2021) Deep learning-based compressed image artifacts reduction based on multi-scale image fusion. Inf Fusion 67:195–207

    Article  Google Scholar 

  • Yilmaz CS, Yilmaz V, Gungor O (2022) A theoretical and practical survey of image fusion methods for multispectral pansharpening. Inf Fusion 79:1–43

    Article  Google Scholar 

  • Zhan L, Zhuang Y, Huang L (2017) Infrared and visible images fusion method based on discrete wavelet transform. J Comput 28(2):57–71

    Google Scholar 

  • Zhang Q, Fu Y, Li H, Zou J (2013) Dictionary learning method for joint sparse representation-based image fusion. Opt Eng 52(5):057006

    Article  Google Scholar 

  • Zhang Q, Wang L, Li H, Ma Z (2011) Similarity-based multimodality image fusion with shiftable complex directional pyramid. Pattern Recognit Lett 32(13):1544–1553

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Electronics & Communication Engineering, UCRD, Chandigarh University, Gharuan, Punjab, India

    Simrandeep Singh

  2. Department of Electronics & Communication Engineering, Department of Research and Innovation, Chandigarh Engineering College, Landran, Punjab, India

    Harbinder Singh & Jaskirat kaur

  3. Division of Research and Innovation, Uttaranchal University, Uttarakhand, India

    Anita Gehlot

  4. Department of Computer Science Engineering, Chandigarh Engineering College, Landran, Punjab, India

    Gagandeep

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  1. Simrandeep Singh
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Singh, S., Singh, H., Gehlot, A. et al. IR and visible image fusion using DWT and bilateral filter. Microsyst Technol 29, 457–467 (2023). https://doi.org/10.1007/s00542-022-05315-7

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