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In packet switched networks, packets may get lost during transmission. As these networks are more and more used for image and video communication, there is a growing need for efficient reconstruction algorithms. In wavelet coded images, the lost coefficients are typically replaced by zeros. This results in annoying black holes in the received image, mainly due to the loss of the low frequency content. In this chapter, we present a novel locally adaptive interpolation method for the reconstruction of the lost low frequency coefficients. We interpolate a lost low frequency coefficient from its four neighbors, and we determine the interpolation weights by the energy of the corresponding coefficients in the high frequency subbands. Compared to older methods of similar complexity, the proposed scheme estimates the lost coefficients much better: the Peak Signal to Noise Ratio is increased with up to 4.3 dB. The results demonstrate a significant improvement of the visual quality. [ABSTRACT FROM AUTHOR]

Multiresolution video denoising is becoming an increasingly popular research topic over recent years. Although several wavelet based algorithms reportedly outperform classical single-resolution approaches, their concepts are often considered as prohibitive for real-time processing. Little research has been done so far towards hardware customization of wavelet domain video denoising. A number of recent works have addressed the implementation of critically sampled orthogonal wavelet transforms and the related image compression schemes in Field Programmable Gate Arrays (FPGA). However, the existing literature on FPGA implementations of overcomplete (non-decimated) wavelet transforms and on manipulations of the wavelet coefficients that are more complex than thresholding is very limited. In this paper we develop FPGA implementation of an advanced wavelet domain noise filtering algorithm, which uses a non-decimated wavelet transform and spatially adaptive Bayesian wavelet shrinkage. The standard composite television video stream is digitalized and used as source for real-time video sequences. The results demonstrate the effectiveness of the developed scheme for real time video processing. [ABSTRACT FROM AUTHOR]