提高超声成像的分辨率

Manuscript received March 12, 2008; revised November 05, 2008. Current version published February 11, 2009. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Bruno Carpentieri. The authors are with the Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1 Canada (e-mail: xwu@ece.mcmaster.ca; zhangxj@grads.ece.mcmaster.ca; wangx28@mcmaster.ca). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TIP.2008.2010638
百度文库
In this paper, we investigate the problem of compact image representation in an approach of sparse sampling in the spatial domain. The fact that most natural images have an exponentially decaying power spectrum suggests the possibility of interpolation-based compact representation of images. A typical scene contains predominantly smooth regions that can be satisfactorily interpolated from a sparsely sampled low-resolution image. The difficulty is with the reconstruction of high frequency contents. Of particular importance is faithful reconstruction of edges without large phase errors, which is detrimental to perceptual quality of a decoded image. To answer the above challenge, we develop a new image compression methodology of collaborative adaptive down-sampling and upconversion (CADU). The CADU approach puts an emphasis on edge reconstruction for the perceptual importance of edges and also to exploit the anisotropy of edge spectrum. In the CADU encoder design, we choose not to perform uneven irregular down sampling of an input image according to local spatial or frequency characteristics. Instead, we stick to conventional square pixel grid by uniform spatial down sampling of the image. Consequently, the resulting low-resolution image can be readily compressed by any of existing image compression techniques, such as DCT-based JPEG and JPEG 2000. Yet the simple uniform down-sampling scheme is made adaptive by a directional low-pass prefiltering step prior to down-sampling. The other purpose of this preprocessing is to induce a mechanism of collaboration between the spatial uniform down-sampling process at the encoder and optimal upconversion process at the decoder. The CADU decoder first decompresses the low-resolution image and then upconverts it to the original resolution in constrained least squares restoration process, using a 2-D piecewise autoregressive model (PAR) and by reversing the directional low-pass prefiltering operation of the encoder. Two-dimensional autoregressive modeling was a known effective technique of predictive image coding [2], [3]. For the CADU decoder, the PAR model plays a role of adaptive noncausal predictor. What is novel and unique of the CADU approach is that the predictor is only used at the decoder side, and the noncausal predictive decoding is performed in collaboration with the prefiltering of the encoder. Fig. 1 presents a block diagram of the proposed CADU image compression system, summarizing the above ideas and depicting the collaboration between the encoder and decoder. The CADU image compression technique, although operating on down-sampled images, obtains some of the best PSNR
I. INTRODUCTION
O
VER the past half century, the prevailing engineering practice of image/video compression, as exemplified by all existing image and video compression standards, is to start with a dense 2-D sample grid of pixels. Compression is done by transforming the spatial image signal into a space (e.g., spaces of Fourier or wavelet bases) in which the image has a sparse representation and by entropy coding of transform coefficients. Recently, researchers in the emerging field of compressive sensing challenged the wisdom of what they called “oversampling followed massive dumping” approach. They showed, quite surprisingly, it is possible, at least theoretically, to obtain compact signal representation by a greatly reduced number of random samples [1].
Xiaolin Wu, Senior Member, IEEE, Xiangjun Zhang, and Xiaohan Wang
Abstract—Recently, many researchers started to challenge a long-standing practice of digital photography: oversampling followed by compression and pursuing more intelligent sparse sampling techniques. In this paper, we propose a practical approach of uniform down sampling in image space and yet making the sampling adaptive by spatially varying, directional low-pass prefiltering. The resulting down-sampled prefiltered image remains a conventional square sample grid, and, thus, it can be compressed and transmitted without any change to current image coding standards and systems. The decoder first decompresses the low-resolution image and then upconverts it to the original resolution in a constrained least squares restoration process, using a 2-D piecewise autoregressive model and the knowledge of directional low-pass prefiltering. The proposed compression approach of collaborative adaptive down-sampling and upconversion (CADU) outperforms JPEG 2000 in PSNR measure at low to medium bit rates and achieves superior visual quality, as well. The superior low bit-rate performance of the CADU approach seems to suggest that oversampling not only wastes hardware resources and energy, and it could be counterproductive to image quality given a tight bit budget. Index Terms—Autoregressive modeling, compression standards, image restoration, image upconversion, low bit-rate image compression, sampling, subjective image quality.
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IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 18, NO. 3, MARCH 2009
Low Bit-Rate Image Compression via Adaptive Down-Sampling and Constrained Least Squares Upconversion