An Integrated Double Hybrid Fusion Approach for Image Smoothing

Abstract

Often in practice, during the process of image acquisition, the acquired image gets degraded due to various factors like noise, motion blur, mis-focus of a camera, atmospheric turbulence, etc. resulting in the image unsuitable for further analysis or processing. To improve the quality of these degraded images, a double hybrid restoration filter is proposed on the two same sets of input images and the output images are fused to get a unified filter in combination with the concept of image fusion. First image set is processed by applying deconvolution using Wiener Filter (DWF) twice and decomposing the output image using Discrete Wavelet Transform (DWT). Similarly, second image set is also processed simultaneously by applying Deconvolution using Lucy–Richardson Filter (DLR) twice followed by the above procedure. The proposed filter gives a better performance as compared to DWF and DLR filters in case of both blurry as well as noisy images. The proposed filter is compared with some standard deconvolution algorithms and also some state-of-the-art restoration filters with the help of seven image quality assessment parameters. Simulation results prove the success of the proposed algorithm and at the same time, visual and quantitative results are very impressive.

Noisy iris smoothing and segmentation scheme based on improved Wildes method

“…Tables 5 and 6 shows PSNR and SNR value of various filters which contains 70dB speckle noise density on nine sampled of eye images having ten variation of each sample. Here, OP (Kumawat and Panda, 2021 ) produces higher value of PSNR and SNR as compare to existing filters which shows the result of image quality. Image quality is better when PSNR as well as SNR is high and when it is low, image quality will be degraded.…”

“…Figures 12 , 13 , 14 , 15 , 16 and 17 shows a comparative analysis of five existing filters i.e. MF (Kumar et al, 2020 ), HMF (Rakesh et al, 2013 ), NAFSM (Kenny and Nor, 2010 ), DAMF (Erkan et al, 2018 ), BPDF (Erkan and Gokrem, 2018 ) with own proposed(op) filter (Kumawat and Panda, 2021 ). At low NDs all these filters give more or less similar result.…”

“…19 Nine sampled segmented speckle noisy images of each person as input from IITDelhi database with varying noise density (ND)70 based on different filters a PSNR; b SNR; c resolution; d FAR

Fig. 20 Nine sampled segmented speckle noisy images of each person as input from IITDelhi database with varying noise density(ND)70 based on different filters using IWM a median filter (Kumar et al, 2020 ); b HMF filter (Rakesh et al, 2013 ); c DAMF filter (Erkan et al, 2018 ); d BPDF filter (Erkan and Gokrem, 2018 ); e NAFSM filter (Kenny and Nor, 2010 ); f own proposed HRFF filter (Kumawat and Panda, 2021 );

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“… Nine sampled segmented speckle noisy images of each person as input from IITDelhi database with varying noise density(ND)70 based on different filters using IWM a median filter (Kumar et al, 2020 ); b HMF filter (Rakesh et al, 2013 ); c DAMF filter (Erkan et al, 2018 ); d BPDF filter (Erkan and Gokrem, 2018 ); e NAFSM filter (Kenny and Nor, 2010 ); f own proposed HRFF filter (Kumawat and Panda, 2021 ); …”

“…13 Five sampled segmenting noisy images of each person as input from IITDelhi database removing noise density 10 based on different filters using IWM a median filter (Kumar et al, 2020 ); b NAFSM filter (Kenny and Nor, 2010 ); c BPDF filter (Erkan and Gokrem, 2018 ); d DAMF filter (Erkan et al, 2018 ); e HMF filter (Rakesh et al, 2013 ); f Own proposed HRFF filter (Kumawat and Panda, 2021 );

Fig. 14 Five sampled segmenting noisy images of each person as input from IITDelhi database removing noise density 30 based on different filters using IWM a median filter (Kumar et al ( 2020 )); b NAFSM filter (Kenny and Nor, 2010 ); c BPDF filter (Erkan and Gokrem, 2018 ); d DAMF filter (Erkan et al, 2018 ); e HMF filter (Rakesh et al, 2013 ); f own proposed HRFF filter (Kumawat and Panda, 2021 )

Fig. 15 Five sampled segmenting noisy images of each person as input from IITDelhi database removing noise density 50 based on different filters using IWM a median filter (Kumar et al, 2020 ); b NAFSM filter (Kenny and Nor, 2010 ); c BPDF filter (Erkan and Gokrem, 2018 ); d DAMF filter (Erkan et al, 2018 ); e HMF filter (Rakesh et al, 2013 ); f own proposed HRFF filter (Kumawat and Panda, 2021 )

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