Barbara Image Research Articles

Adaptive Multiscale Block Compressed Sensing of Images based on Gray Level Co- Occurrence Matrix

In conventional block compressed sensing (BCS), the images are divided into small fixed-size blocks sampled at the same sub-rate. The sparsities and high-frequency components of the images are ignored, and the reconstruction qualities of the complex texture images are poor. An adaptive multiscale variant of the block compressed sensing was proposed to reconstruct the texture details of the images. The texture features of the images were obtained from the high-frequency components by the three-level wavelet transform and analyzed on the basis of the gray level co-occurrence matrix. A mathematical model was established to adjust the block sizes of the images automatically and allocate the limited sampling resource adaptively. The smoothed projected Landweber (SPL) was utilized to reconstruct the images. The accuracy of the proposed algorithm was verified by the simulation experiments. Results demonstrate that the texture details of the reconstructed images are abundant. The image edges are also clear, and the blocking artifacts are effectively eliminated. The reconstruction qualities of images, especially the partial images, are considerably improved at different sub-sampling rates. The proposed algorithm achieves a 2.42–3.3 dB gain in reconstruction PSNR for the Barbara image over the original BCS-SPL at a sub-sampling rate of 0.3. No remarkable differences are noted between the reconstructed and original texture blocks in visual sensation. The proposed algorithm provides evidence for the compression and reconstruction of the images with complex texture details.

An Improved Method on Non-subsampled Contourlet Transform Ultrasonic Image Denoising

The non-subsampled contourlet transform (NSCT) is a good approach in capturing the detail of images, so that it has a wide range of applications in image denoising. In order to improve the denoising effect of NSCT method of medical ultrasound images, the local intuitionistic fuzzy entropy (LIFE) theory is employed in this work to suppress the noise in the high-frequency coefficients of NSCT. On combination with the LIFE theory and the directionality of the coefficients’ distribution of NSCT, the directional local intuitionistic fuzzy entropy (DLIFE) method has been put forward to calculate the corresponding noise suppression factor to remove the noise in the high-frequency coefficients of NSCT. The experiments on both Barbara image and medical ultrasound image show a better denoising effect than the original NSCT soft threshold method and the wavelet soft threshold method.

Enhanced encrypted reversible data hiding algorithm with minimum distortion through homomorphic encryption

Reversible data hiding means embedding a secret message in a cover image in such a manner, to the point that in the midst of extraction of the secret message, the cover image and, furthermore, the secret message are recovered with no error. The goal of by far most of the reversible data hiding algorithms is to have improved the embedding rate and enhanced visual quality of stego image. An improved encrypted-domain-based reversible data hiding algorithm to embed two binary bits in each gray pixel of original cover image with minimum distortion of stego-pixels is employed in this paper. Highlights of the proposed algorithm are minimum distortion of pixel’s value, elimination of underflow and overflow problem, and equivalence of stego image and cover image with a PSNR of ∞ (for Lena, Goldhill, and Barbara image). The experimental outcomes reveal that in terms of average PSNR and embedding rate, for natural images, the proposed algorithm performed better than other conventional ones.

Improved Interscale-Based SURE-LET Approach to Image Denoising

Currently, the SURE-LET approach has been widely used for the removal of noise in corrupted image. However the original interscale-based SURE-LET approach rules out intrascale considerations of wavelet coefficients, which makes the denoising ability of the technique is reduced greatly. In this paper, the local Wiener-like estimator is incorporated into the original interscale-based SURE-LET thresholding function. The tests show the improved interscale-based SURE-LET gets the better denoising performance objectively and subjectively. Especially for Barbara image, the PSNR is on average improved by 0.52dB compared to the traditional pointwise interscale-based SURE-LET approach.

对称分数样条小波域图像去噪研究

According to data smooth theory, the best trade-off fractional order of 7.9, which the symmetric fractional B-spline wavelets can achieve between its smoothness and approximation texture, is deduced. A new thresholding formula which has self-adaptability with the order of the fractional spline wavelet, is derived, through improving the wavelet denosing model based on biva-Shrink neighboring coefficients relevance. Fractional spline wavelet transform is a new powerful tool for removing noise and irrelevant information from denoised images. Experimental result testifies the obtained theoretic result. Comparied with traditional denoising algorithms, it can achieve higher subjective and objective image qualities, especially for texture images. When the Barbara image with variance is no more than 10, the peak signal-to-noise ratio of the denoised image can reach 34.9842 and its geometric textures are well-protected after denoising by this algorithm.

Constraints of Second-Order Vanishing Moments on Lattice Structures for Non-separable Orthogonal Symmetric Wavelets

In this paper, a design method of two-dimensional (2-D) orthogonal symmetric wavelets is proposed by using a lattice structure for multi-dimensional (M-D) linear-phase paraunitary filter banks (LPPUFB), which the authors have proposed as a previous work and then modified by Lu Gan et al. The derivation process for the constraints on the second-order vanishing moments is shown and some design examples obtained through optimization with the constraints are exemplified. In order to verify the significance of the constraints, some experimental results are shown for Lena and Barbara image.

Image compression by sparse PCA coding in curvelet domain

This paper uses the newly developed concept of ‘sparsity’ in signal processing to the context of Image Compression. The first step of the scheme is to use a sparsifying transform on the image. The sparse set of coefficients is encoded via Sparse PCA. Wavelet Transform had been used profusely for image compression tasks. But the choice is not the ideal one. The partial reconstruction error from wavelet coefficients is an order of magnitude higher than the ideal error rate. In this paper image compression is carried in the curvelet domain—a better choice compared to wavelets, at least theoretically, since the reconstruction error rate with curvelet coefficients is of the same asymptotic order as that of the ideal error rate. The compression scheme is tested on the Lena and Barbara image as well as the USPS and Yale Face databases.

A New Interscale and Intrascale Orthonormal Wavelet Thresholding for SURE-Based Image Denoising

The interscale Stein's unbiased risk estimator (SURE)-based approach introduced by Luisier is a recent state of the art in orthonormal wavelet denoising, but it is not very effective for those images that have substantial high-frequency contents. To solve this problem, we introduce an effective integration of the intrascale correlations within the interscale SURE-based approach. We show that the consideration of both the intrascale and interscale dependencies of wavelet coefficients brings more denoising gains than those obtained with the interscale SURE-based approach, especially for denoising of images that have substantial textures such as the Barbara image.

Medical ultrasound image compression using joint optimization of thresholding quantization and best-basis selection of wavelet packets

This paper introduces an efficient image-coding algorithm using wavelet packets. The algorithm combines the top-down search approach with an operational rate-distortion (R-D) cost function to select the best wavelet packet basis at low-computational cost. The proposed method jointly optimizes the best-basis selection, coefficient ''thresholding'' and quantizer selection within the minimum description length (MDL) framework to develop a wavelet packet image coder named as JTQ-WP. We present results to verify the usefulness and versatility of this adaptive image coder both on medical US-images and natural images. The experimental results show that the joint optimization has a dramatic effect on the compression performance of medical ultrasound images. To further demonstrate the potential performance of the proposed method in comparison with the current state-of-the-art image coding algorithms, the results on Barbara image are also presented. The results show a coding gain of 0.91 dB over the benchmark wavelet-coding algorithm, SPIHT, on the Barbara image at a bit-rate of 0.25 bpp.

Low-complexity and low-memory entropy coder for image compression

A low-complexity and low-memory entropy coder (LLEC) is proposed for image compression. The two key elements in the LLEC are zerotree coding and Golomb-Rice (1966, 1991) codes. Zerotree coding exploits the zerotree structure of transformed coefficients for higher compression efficiency. G-R codes are used to code the remaining coefficients in a variable-length codes/variable-length integer manner resulting in JPEG similar computational complexity. The proposed LLEC does not use any Huffman table, significant/insignificant list, or arithmetic coding, and therefore its memory requirement is minimized with respect to any known image entropy coder. In terms of compression efficiency, the experimental results show that discrete cosine transform (DCT)- and discrete wavelet transform (DWT)-based LLEC outperforms baseline JPEG and embedded zerotree wavelet coding (EZW) at the given bit rates, respectively. For example, LLEC outperforms baseline JPEG by an average of 2.2 dB on the Barbara image and is superior to EZW by an average of 0.2 dB on the Lena image. When compared with set partition in hierarchical trees, LLEC is inferior by 0.3 dB, on average, for both Lena and Barbara. In addition, LLEC has other desirable features, such as parallel processing support, region of interest coding, and as a universal entropy coder for DCT and DWT.

Significance-linked connected component analysis for wavelet image coding

Recent success in wavelet image coding is mainly attributed to a recognition of the importance of data organization and representation. There have been several very competitive wavelet coders developed, namely, Shapiro's (1993) embedded zerotree wavelets (EZW), Servetto et al.'s (1995) morphological representation of wavelet data (MRWD), and Said and Pearlman's (see IEEE Trans. Circuits Syst. Video Technol., vol.6, p.245-50, 1996) set partitioning in hierarchical trees (SPIHT). We develop a novel wavelet image coder called significance-linked connected component analysis (SLCCA) of wavelet coefficients that extends MRWD by exploiting both within-subband clustering of significant coefficients and cross-subband dependency in significant fields. Extensive computer experiments on both natural and texture images show convincingly that the proposed SLCCA outperforms EZW, MRWD, and SPIHT. For example, for the Barbara image, at 0.25 b/pixel, SLCCA outperforms EZW, MRWD, and SPIHT by 1.41 dB, 0.32 dB, and 0.60 dB in PSNR, respectively. It is also observed that SLCCA works extremely well for images with a large portion of texture. For eight typical 256x256 grayscale texture images compressed at 0.40 b/pixel, SLCCA outperforms SPIHT by 0.16 dB-0.63 dB in PSNR. This performance is achieved without using any optimal bit allocation procedure. Thus both the encoding and decoding procedures are fast.

Image-adaptive vector quantization in an entropy-constrained framework

An adaptive vector quantization (VQ) scheme with codebook transmission is derived for the variable-rate source coding of image data using an entropy-constrained Lagrangian framework. Starting from an arbitrary initial codebook C(I) available to both the encoder and decoder, the proposed algorithm iteratively generates an improved operational codebook C(0) that is well adapted to the statistics of a particular image or subimage. Unlike other approaches, the rate-distortion trade-offs associated with the transmission of updated code vectors to the decoder are explicitly considered in the design. In all cases, the algorithm guarantees that the operational codebook C(0) will have rate-distortion performance (including all side-information) better than or equal to that of any initial codebook C(I). When coding the Barbara image, improvement at all rates is demonstrated with observed gains of up to 3 dB in peak signal-to-noise ratio (PSNR). Whereas in general the algorithm is multipass in nature, encoding complexity can be mitigated without an exorbitant rate-distortion penalty by restricting the total number of iterations. Experiments are provided that demonstrate substantial rate-distortion improvement can be achieved with just a single pass of the algorithm.