This paper describes an efficient and adaptive method of threshold estimation for removing impulse noise from images, based on Double Density Wavelet Transform (DDWT). The performance of image de-noising algorithms using wavelet transforms can be improved significantly by fixing an optimum threshold value, based on the analysis of the statistical parameters of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analyzing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. Here the noisy image is first decomposed into many levels to obtain different frequency bands using DDWT. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum threshold value by the proposed method. Experimental results on several test images by using the proposed method show that, the proposed method yields significantly superior image quality and better Peak Signal-to-Noise Ratio (PSNR). Some comparisons with the best available results will be given in order to illustrate the effectiveness of the proposed algorithm.

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Summary Wireless Sensor Networks (WSN) have gained considerable research interest due to its wider applicability and efficiency. Due to the technological advancement, the WSN is multifaceted and one of the facets is the Wireless Visual Sensor Network (WVSN). The core functionality of WVSN is to handle multimedia data that involves data processing and transference, which, in turn, consumes more energy. Data transference requires more energy and the energy consumption is reduced by the concept of compressive sensing. Understanding the benefits of compressive sensing, this article proposes an adaptable compressive scheme that relies on the texture property of the video frames. The texture property is extracted by means of Local Directional Pattern (LDP). The performance of the proposed approach is evaluated in terms of image quality, time, and energy consumption. The proposed approach outperforms the comparative approaches with reasonable performance.

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