Sh. Mahmoudi-Barmas, Sh. Kasaei,
Volume 4, Issue 1 (1-2008)
Abstract
Image registration is a crucial step in most image processing tasks for which the
final result is achieved from a combination of various resources. In general, the majority of
registration methods consist of the following four steps: feature extraction, feature
matching, transform modeling, and finally image resampling. As the accuracy of a
registration process is highly dependent to the feature extraction and matching methods, in
this paper, we have proposed a new method for extracting salient edges from satellite
images. Due to the efficiency of multiresolution data representation, we have considered
four state-of-the-art multiresolution transforms –namely, wavelet, curvelet, complex
wavelet and contourlet transform- in the feature extraction step of the proposed image
registration method. Experimental results and performance comparison among these
transformations showed the high performance of the contourlet transform in extracting
efficient edges from satellite images. Obtaining salient, stable and distinguishable features
increased the accuracy of the proposed registration process.
E. Ehsaeyan,
Volume 12, Issue 1 (3-2016)
Abstract
The use of wavelets in denoising, seems to be an advantage in representing well the details. However, the edges are not so well preserved. Total variation technique has advantages over simple denoising techniques such as linear smoothing or median filtering, which reduce noise, but at the same time smooth away edges to a greater or lesser degree. In this paper, an efficient denoising method based on Total Variation model (TV), and Dual-Tree Complex Wavelet Transform (DTCWT) is proposed to incorporate both properties. In our method, TV is employed to refine low-passed coefficients and DTCWT is used to shrink high-passed noisy coefficients to achieve more accurate image recovery. The efficiency of our approach is firstly analyzed by comparing the results with well-known methods such as probShrink, BLS-GSM, SUREbivariate, NL-Means and TV model. Secondly, it is compared to some denoising methods, which have been reported recently. Experimental results show that the proposed method outperforms the Steerable pyramid denoising by 8.5% in terms of PSNR and 17.5% in terms of SSIM for standard images. Obtained results convince us that the proposed scheme provides a better performance in noise blocking among reported state-of-the-art methods.
