A robust approach for denoising and enhancement of mammographic images contaminated with high density impulse noise

Jain, Akshat; Singh, Sonam; Bhateja, Vikrant

doi:10.1504/ijconvc.2013.054659

Cited by 61 publications

(8 citation statements)

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“…FPGA based video stabilisation was proposed by Li 30 , where Kalman filter was used effectively. Very good image denoising and quality assessment is provided in [31][32][33][34][35][36][37][38][39][40][41] .…”

Section: Related Workmentioning

confidence: 99%

Efficient Embedded Hardware Architecture for Stabilised Tracking Sighting System of Armoured Fighting Vehicles

Verma¹,

Ghosh²,

Marathe³

et al. 2019

Def. Sc. Jl.

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A line-of-sight stabilised sighting system, capable of target tracking and video stabilisation is a prime requirement of any armoured fighting tank vehicle for military surveillance and weapon firing. Typically, such sighting systems have three prime electro-optical sensors i.e. day camera for viewing in day conditions, thermal camera for night viewing and eye-safe laser range finder for obtaining the target range. For laser guided missile firing, additional laser target designator may be a part of sighting system. This sighting system provides necessary parameters for the fire control computer to compute ballistic offsets to fire conventional ammunition or fire missile. System demands simultaneous interactions with electro-optical sensors, servo sensors, actuators, multi-function display for man-machine interface, fire control computer, logic controller and other sub-systems of tank. Therefore, a complex embedded electronics hardware is needed to respond in real time for such system. An efficient electronics embedded hardware architecture is presented here for the development of this type of sighting system. This hardware has been developed around SHARC 21369 processor and FPGA. A performance evaluation scheme is also presented for this sighting system based on the developed hardware.

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Section: Related Workmentioning

confidence: 99%

Efficient Embedded Hardware Architecture for Stabilised Tracking Sighting System of Armoured Fighting Vehicles

Verma¹,

Ghosh²,

Marathe³

et al. 2019

Def. Sc. Jl.

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“…However, the proposed system incorporates weight function that requires more computational time. Jain et al proposed a combined technique for suppression of high‐density IN and then enhancement of the contrast of mammographic breast lesions in medical imaging. Proposed noise removal method had applications iteratively to efficiently remove the IN.…”

Section: Introductionmentioning

confidence: 99%

Efficient impulse noise removal using hybrid neuro‐fuzzy filter with optimized intelligent water drop technique

Devi¹,

Soranamageswari²

2019

Int J Imaging Syst Tech

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Impulse noise (IN) affects the digital image, during transmission, digital storage, and image acquisition. IN removal from an image is necessary as it retains the quality of the image. This work concentrates on the IN. A neuro‐fuzzy (NF) system based on a fuzzy technique which is trained by a learning algorithm derived from neural network theory was implemented for the removal of noise. A NF network for noise filtering in grayscale images that combines two NF filters with a postprocessor to produce the output was presented. However, Sugeno‐type is not intuitive technique and it also less accurate. To overcome these problems, a hybrid NF filter with optimized intelligent water drop (IWD) technique is introduced, where hybridized Sugeno–Mamdani‐based fuzzy interference system is implemented in both the NF filters to obtain more efficient noise removal system. To improve the accuracy of the assignment of membership values to each input pixels, the optimized IWD technique is utilized, as the choice of membership function decides the efficiency of the noise removal in the images. Here, Fuzzy rules have been used to obtain the filtered output. The Hybrid method maintains the accuracy of the Sugeno model and also the interpretable capability of the Mamdani model. This method is robust against the IN and it is flexible, efficient, and accurate than existing filtering method in both noise attenuation and detail preservation and it has a great scope for better real‐time applications.

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“…Hence, the wavelets preserve time and frequency information, thereby yielding a suitable approach for medical image fusion. Further, these fused images are also processed with denoising [19]- [20], contrast [21]- [30] and edge enhancement [31]- [33] techniques to improve upon the visualization of diagnostic information. The proposed work therefore presents a combination of wavelet transform and PCA as an improvement to the aforementioned .com limitations.…”

Section: Introductionmentioning

confidence: 99%

Medical image fusion using combination of PCA and wavelet analysis

Krishn

Bhateja

Himanshi

et al. 2014

2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)

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Medical image fusion for merging of complementary diagnostic content has been carried out in this paper using Principal Component Analysis (PCA) and Wavelets. The proposed fusion approach involves sub-band decomposition using 2D-Discrete Wavelet Transform (DWT) in order to preserve both spectral and spatial information. Further, PCA is applied on the decomposed coefficients to maximize the spatial resolution. An optimal variant of the daubechies wavelet family has been selected experimentally for better fusion results. Simulation results demonstrate an improvement in visual quality of the fused image in comparison to other state-of-art fusion approaches.

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A robust approach for denoising and enhancement of mammographic images contaminated with high density impulse noise

Cited by 61 publications

References 0 publications

Efficient Embedded Hardware Architecture for Stabilised Tracking Sighting System of Armoured Fighting Vehicles

Efficient Embedded Hardware Architecture for Stabilised Tracking Sighting System of Armoured Fighting Vehicles

Efficient impulse noise removal using hybrid neuro‐fuzzy filter with optimized intelligent water drop technique

Medical image fusion using combination of PCA and wavelet analysis

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