Deyun Yang

Comments on "Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering

In order to resolve the problem that the denoising performance has a sharp drop when noise standard deviation reaches 40, proposed to replace the wavelet transform by the DCT. In this comment, we argue that this replacement is unnecessary, and that the problem can be solved by adjusting some numerical parameters. We also present this parameter modification approach here. Experimental results demonstrate that the proposed modification achieves better results in terms of both peak signal-to-noise ratio and subjective visual quality than the original method for strong noise.

A multi-channel watermarking scheme based on HVS and DCT-DWT

With the rapid growth of the Internet and multimedia processing techniques, digital data becomes much easier to obtain which brings the problem of copyright protection. Among various ways, digital watermarking has been proven to be an effective solution to the problem of copyright infringement. This paper proposes a new blind watermarking algorithm for digital images based on DCT and DWT. First, the original image is transformed from the RGB color spaces into the YCbCr color spaces in which the watermark is embedded into all the three color spaces. Second, the position in which watermark was embedded was chosen by Human Visual System (HVS), and the watermarking embedding is achieved by the proposed algorithm based on DCT and DWT. Experimental result indicates that the algorithm is visually imperceptible and robust to general image processing.

Image denoising by block-matching and 1D filtering

In this paper, we develop a new image denoising method based on block-matching and transform-domain filtering. The developed method is derived from the current state-of-the-art denoising method (BM3D). We separate the 3D transform in the original method to two steps 1D transform, to further enhance the sparsity for signals whose elements are highly similar and to weaken the sparsity for those signals whose elements are dissimilar. Because the 1D filtering is on highly similar elements and the 2D filtering on image blocks are all removed, the image details can be better reserved and fewer artifacts are introduced than original method. Experimental results demonstrate that the developed method is competitive and better than some of the current state-of-the-art denoising methods in terms of peak signal-to-noise ratio, structural similarity, and subjective visual quality.

Texture Classification Using Nonsubsampled Contourlet Transform and LS-SVM

The paper proposed a novel algorithm for texture classification system. This texture classification system is based on the extracted features on the performance of texture images’ Nonsubsampled Contourlet Transform (NSCT). To decrease the dimension of feature vector, we achieve the mean and standard deviation of NSCT coefficients matrix in different subbands and various directions. To compare the performance of the proposed algorithm, the other two commonly used algorithms including of Wavelet Package Transform and the improved LBP descriptor are used to extract texture features. This paper presents the application of Least Square Support Vector Machine (LS-SVM) classifiers to realize the automatic texture classification. The performed numerical experiments show that our algorithm produces a marked improvement in classification performance, which suggests a significant advance in texture classification.

Semisubsampled Wavelet Transform Based Image Watermarking with Strong Robustness to Rotation Attacks

In this paper, we develop a novel transform called semisubsampled wavelet transform (SSWT) and employ it to image watermarking. SSWT consists of two parts, one is nonsubsampled tight frame transform, the other is critically sampled wavelet transform (WT). Embedding watermark into the low-frequency sub-band of SSWT, the imperceptibility and robustness of watermark can be significantly improved comparing with some existing watermarking schemes. Experimental results show that proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, Wiener filtering and median filtering attacks. For rotation attack, we propose a novel watermarking resynchronization approach, the ideal watermark can be always successfully extracted after resynchronization operation to any angle rotated watermarked image. Experimental results show that the proposed resynchronization approach is considerably effective and feasible.

Semisubsampled wavelet transform based image watermarking

In this paper, we develop a novel transform which is called semisubsampled wavelet transform (SSWT) and employ it to image watermarking. Combining nonsubsampled pyramid (NSP) with critically sampled wavelet transform (WT) to construct filter banks of SSWT. The result of the nonsubsapled part is a flexible multiscale and shift invariant image decomposition that can be efficiently implemented via the à trous algorithm. Because of the shift invariance can efficiently remove the Gibbs phenomenon, the imperceptibility and robustness of watermark can be improved simultaneously. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, Wiener filtering and median filtering attacks. The comparison analysis demonstrates that our scheme has better performance than the watermarking schemes reported recently.