Representation Of Edges In Images Research Articles

AN EFFICIENT RIPPLET-BASED SHRINKAGE TECHNIQUE FOR MR IMAGE RESTORATION

In this paper a new ripplet-based shrinkage technique is used to suppress noise from Magnetic Resonance Imaging (MRI). The propitious properties of ripplet transform such as anisotropy, high directionality, good localization, and high-energy compaction make the proposed method efficient and feature preserving when compared to other transforms. Ripplet transform provides efficient representation of edges in images with a higher potential for image processing applications such as image restoration, compression, and de-noising. The proposed method implies a new nonlinear ripplet-based shrinkage technique to extract the spatial and frequency information from MRI corrupted by noise. The choice of this new shrinkage technique is due to its simplicity, versatility, and its efficiency in removing noise from homogenous regions and those regions with singularities, when compared to the existing filtering techniques. Experiments were conducted on several diffusion weighed images and anatomical images. The results show that the proposed de-noising technique shows competitive performance compared to the current state-of-art methods. Qualitative validation was performed based on several quality metrics and profound improvement over existing methods was obtained. Higher values of Peak Signal to Noise Ratio (PSNR), Correlation Coefficient (CC), mean structural similarity index (MSSIM), and lower values of Root Mean Square Error (RMSE) and computational time were obtained for the proposed ripplet-based shrinkage technique when compared to the existing ones.

Directional lifting wavelet and universal trellis-coded quantisation-based image coding algorithm and objective quality evaluation

In this study, an image coding algorithm based on directional lifting wavelet transform (DLWT) and universal trellis-coded quantisation (UTCQ) is presented, and the coding performance is evaluated with three objective image quality metrics. Compared to the discrete wavelet transform, DLWT performing prediction and update along the direction of the local region can provide an efficient representation of edges in images, but shows a similar ability in representing the smooth region. To further improve the visual quality of the smooth background regions, UTCQ is adopted to quantising the wavelet coefficients. The proposed algorithm is measured with not only the dominant peak signal-to-noise ratio (PSNR), but also new metrics multi-scale structural similarity index measure (MSSIM) and visual information fidelity (VIF) which provide a better approximation to the perceived image quality than PSNR by taking the property of human visual system (HVS) into account. Experimental results show that the proposed algorithm has the best MSSIM and VIF performance among the compared algorithms (including JPEG2000) for the typical test images, and its decoded images at low bit-rate are visually more appealing in both edges and smooth background regions. For image Barbara, the proposed algorithm outperforms JPEG2000 up to 24.63% relatively in VIF and 1.93%dB in PSNR at 0.5%bpp, at most 3.62% relatively in MSSIM at 0.125%bpp. The experimental results also show that UTCQ does perform better than scalar quantisation (SQ) in MSSIM and VIF and improves the subjective visual quality, although UTCQ is not necessarily better than SQ in PSNR.

Ripplet: A new transform for image processing

Efficient representation of images usually leads to improvements in storage efficiency, computational complexity and performance of image processing algorithms. Efficient representation of images can be achieved by transforms. However, conventional transforms such as Fourier transform and wavelet transform suffer from discontinuities such as edges in images. To address this problem, we propose a new transform called ripplet transform. The ripplet transform is a higher dimensional generalization of the curvelet transform, designed to represent images or two-dimensional signals at different scales and different directions. Specifically, the ripplet transform allows arbitrary support c and degree d while the curvelet transform is just a special case of the ripplet transform (Type I) with c = 1 and d = 2. Our experimental results demonstrate that the ripplet transform can provide efficient representation of edges in images. The ripplet transform holds great potential for image processing such as image restoration, image denoising and image compression.

Stable Edge-Adaptive Multiscale Decompositions Using Updated Normal Offsets

The normal offset decomposition is an adaptive (nonlinear) multiscale data transform, constructed in a way similar to the lifting scheme for wavelet transforms. The main difference lies in the fact that the detail coefficients are not wavelet coefficients corresponding to a wavelet basis function. Wavelet coefficients carry detail information situated at the location of the basis function. Besides detail information, normal offset coefficients also contain directional/geometrical information, i.e., information on where to locate the details. The normal offset decomposition is a jump-adaptive transform leading to a sparse representation of edges in images and sharp reconstructions of these edges. A major issue in this paper is the design of a new class of wavelet transforms that can be extended to normal offset decompositions. The combination of normal offsets and a stable lifting scheme is nontrivial. A necessary condition for an -stable lifted wavelet transform is that the wavelet basis functions have a vanishing integral (i.e., at least one vanishing moment). Although the normal offset decomposition is not a basis transform in the strict sense, a similar criterion for stability can be established and instability is equally a problem in a naive implementation. This paper constructs an ldquoupdatedrdquo normal offset decomposition that is stable and therefore appropriate for use in applications that sensitive to the numerical condition, such as applications involving (heavy) noise. The new scheme combines numerical stability, fast computation, and sharp edge representations.

Adaptive edge image enhancement based on maximum fuzzy entropy

Based on the maximum fuzzy entropy principle, the edge image with low contrast is optimally classified into two classes adaptively, under the condition of probability partition and fuzzy partition. The optimal threshold is used as the classified threshold value, and a local parametric gray-level, transformation is applied to the obtained classes. By means of two parameters representing, the homogeneity of the regions in edge image is improved. The excellent performance of the proposed technique is exercisable through simulation results on a set of test images. It is shown how the extracted and enhanced edges provide an efficient edge-representation of images. It is shown that the proposed technique prossesses excellent performance in homogeneity through simulations on a set of test images, and the extracted and enhanced edges provide an efficient edge-representation of images.