With stereo audio stepping into people's daily lives, spatial parametric technology comes to its broad prospects for development. This paper provides the audio bandwidth extension encoding and decoding devices, the bitrate of less high-frequency signal reconstruction, in order to improve the quality of the output audio signal. This research makes a contribution to the lack of existing technology, audio bandwidth extension encoding and decoding devices, digital audio decoding to take advantage of a lower bit rate to restore the original audio signal in the digital encoding process the loss of high-frequency components, thereby enhancing the sound playback quality.

Research on audio bandwidth extension codec

Abstr act. Image super-resolution (SR) is a very useful technique for visual surveillance, high-definition TV and medical image processing. In the traditional method, the inferred high-resolution image patch is represented as a linear combination of dictionaries obtained by training images, and least square estimation or sparse regularization is used to find better solution. However, the methods proposed so far neglect the prior knowledge about local structure similarity in natural images. In this paper we introduce an adaptive regularization terms into LLE based SR framework. First, an example based image nonlocal mean regularization term is learned from the dataset of example image patches, which captures the local structure similarity between the input image and training images. Then, the image nonlocal self-similarity is used as another regularization term. In addition, we propose an iterative optimization framework to find the latent HR image. Experimental results on real surveillance images demonstrate the superiority of the proposed method over some state-of-the-art image SR approaches.

/pdf/an-example-based-non-local-mean-regularization-for-image-ttpa7rpezn.pdf

Li Chuanhai

Papers

Research on audio bandwidth extension codec

An Example Based Non-local Mean Regularization For Image Super-resolution