RETRACTED: Contourlet transform and adaptive neuro-fuzzy strategy–based color image watermarking

Abstract

In the today Internet era, protection of digital content during transmission is an indigent. Alphanumeric watermarking is a resolution to the copyright defense than the endorsement of information into the system. In exhibit watermarking calculation, wellbeing of such watermarking process is moderately low. For expanding the soundness, an approach is presented, which is contourlet change with neuro-fuzzy-based watermark inserting process. The conventional approaches having loss during data recovery, this situation will be resolved using proposed watermarking scheme and also increase the security of watermarked image. The proposed color image watermarking scheme binary image is embedded over the shading image which utilizes contourlet and converse contourlet calculation for preprocessing of an image and neuro-fuzzy calculation to implant the bits in the green plane of an image. After completing the watermarking process, the results are analyzing using the quality assessment metrics like Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) etc., It is implemented using MATLAB R2013 software. The developed MATLAB code is converted into Hardware Description Language (HDL) and then implemented for Virtex-5 L110T Field Programmable Gate Array (FPGA) kit.

Combination of Fast Finite Shear Wave Transform and Optimized Deep Convolutional Neural Network: A Better Method for Noise Reduction of Wetland Test Images

“…However, when dealing with images with a high density of salt-and-pepper noise, this method may result in the image becoming more blurred. Senthilkumar et al [13] proposed the use of the contourlet transform to effectively extract the geometric features and contour information of an image, and further improve the image quality. By merging singularities into the same coefficients, noise and discontinuities in the image can be reduced, resulting in clearer and smoother image results.…”