Despeckling of ultrasound images using non-linear conductance function

Bhateja, Vikrant; Singh, Gopal; Srivastava, Atul; Singh, Jay

doi:10.1109/spin.2014.6777049

Cited by 11 publications

(3 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The proposed metric can be further extended for contrast and sharpness measurement using non-linear operators [56]- [59]. This can be therefore implemented for IQA to evaluate the performance of computer aided analysis systems for breast cancer [60]- [63]. …”

Section: Resultsmentioning

confidence: 99%

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

et al. 2015

Self Cite

View full text Add to dashboard Cite

Application specific information processing (ASIP) unit in smart cameras requires sophisticated image processing algorithms for image quality improvement and extraction of relevant features for image understanding and machine vision. The improvement in performance as well as robustness can be achieved by intelligent moderation of the parameters both at algorithm (image resolution, contrast, compression, and so on) as well as hardware levels (camera orientation, field of view, and so on). This paper discusses the employment of ISO/IEC/IEEE 21451 smart transducer standards for performance improvement of smart cameras. The standardized transducer electronic data sheets (TEDS-by IEEE 21450) provide the self description of sensors, of which the calibration details are of vital importance to yield a smart and reconfigurable imaging system. This is possible by exercising intelligent control over the TEDS (smart camera) calibration details as well as automated tuning of algorithm parameters (in ASIP) based on decisions by perceptually efficient image quality assessment (IQA) tool. Estimation of distortion based on reduced reference IQA has been highlighted as a reliable methodology for this purpose. The proposed IQA approach uses wavelets for features extraction followed by estimation of luminance, contrast, and divergence parameters to obtain the proposed distortion measure (Q). The computational complexity in the process has been catalyzed using integral image and gradient magnitude approaches. The validation of Q metric is carried out by evaluating the image quality for various types of distortions on images from Content-based Strategies of Image Quality assessment (CSIQ) and Information Visualization CyberInfrastructure (IVC) databases. Simulation results yield a healthy correlation of Q and the subjective human opinions.

show abstract

Section: Resultsmentioning

confidence: 99%

A Reduced Reference Distortion Measure for Performance Improvement of Smart Cameras

et al. 2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…The said wavelet family works well in order to retain the features of the SAR image. Lastly, the obtained reconstructed images are evaluated in terms of various objective quality evaluation parameters namely SSIM (Structural Similarity), PSNR (Peak Signal to Noise Ratio in dB) and SSI (Speckle Suppression Index) [17], [37], [39]. SSIM is computed to assess the degree of preservation of structural content in the filtering SAR images.…”

Section: Methodsmentioning

confidence: 99%

“…In SAR systems, the speckle noise is generally referred as the difference between the measured and true mean values of the image pixels which degrade the visual quality of the images. Here, the statistics of speckle noise is considered to be independent of the nature of signal and noise [13]- [17]. Speckle reduction is needed for two main reasons: firstly to improve the human interpretation, or for visual enhancement and secondly, it forms mandatory pre-processing step for many image processing tasks.…”

Section: Introductionmentioning

confidence: 99%