Inverse Halftoning Based on Weighted Nuclear Norm Minimization

The inverse halftoning technology refers to restore a continuous-tone image from a halftone image with only bi-level pixes. However, recovering the continuous images from their halftoned ones is normally ill-posed, which making the inverse halftoning algorithm very challenging. In this paper, we propose an optimization model with two alternate projections (TAP) for image inverse halftoning under the weighted nuclear norm minimization (WNNM) framework. The main contributions are two-folds. First, the WNNM nonlocal regularization term is established, which offers a powerful mechanism of nonlocal self-similarity to ensure a more reliable estimation. Second, the alternate minimization projections are formulated for solving the image inverse halftoning, which reconstructs the continuous-tone image without destroying the image details and structures. The experiment results shown that the proposed method outperformed the state of the arts in terms of both objective measurements and subjective visual performance.

Recovering Texture with a Denoising-Process-Aware LMMSE Filter

Image denoising methods generally remove not only noise but also fine-scale textures and thus degrade the subjective image quality. In this paper, we propose a method of recovering the texture component that is lost under a state-of-the-art denoising method called weighted nuclear norm minimization (WNNM). We recover the image texture with a linear minimum mean squared error estimator (LMMSE filter), which requires statistical information about the texture and noise. This requirement is the key problem preventing the application of the LMMSE filter for texture recovery because such information is not easily obtained. We propose a new method of estimating the necessary statistical information using Stein’s lemma and several assumptions and show that our estimated information is more accurate than the simple estimation in terms of the Fréchet distance. Experimental results show that our proposed method can improve the objective quality of denoised images. Moreover, we show that our proposed method can also improve the subjective quality when an additional parameter is chosen for the texture to be added.

NON-LINEAR MULTI-FRAME IMAGE DENOISING USING WEIGHTED NUCLEAR NORM MINIMIZATION

We address the problem of constructing single low noise image from a sequence of multiple noisy images. We use the approach based on finding and averaging similar blocks in the image and extend it to multiple images. Unlike traditional multi-frame super-resolution algorithms, the block-matching approach does not require computationally expensive motion estimation for multi-frame image denoising. In this work, we use an algorithm based on weighted nuclear minimization for image denoising. The evaluation of the algorithm shows noticeable improvement of image quality when using multiple input images instead of single one. The improvement is the most noticeable in the areas with complex non-repeated structure.

Sobel Edge Detection Based on Weighted Nuclear Norm Minimization Image Denoising

As a classic and effective edge detection operator, the Sobel operator has been widely used in image segmentation and other image processing technologies. This operator has obvious advantages in the speed of extracting the edge of images, but it also has the disadvantage that the detection effect is not ideal when the image contains noise. In order to solve this problem, this paper proposes an optimized scheme for edge detection. In this scheme, the weighted nuclear norm minimization (WNNM) image denoising algorithm is combined with the Sobel edge detection algorithm, and the excellent denoising performance of the WNNM algorithm in a noise environment is utilized to improve the anti-noise performance of the Sobel operator. The experimental results show that the optimization algorithm can obtain better detection results when processing noisy images, and the advantages of the algorithm become more obvious with the increase of noise intensity.

Weighted Nuclear Norm Minimization on Multimodality Clustering

Generally, multimodality data contain different potential information available and are capable of providing an enhanced analytical result compared to monosource data. The way to combine the data plays a crucial role in multimodality data analysis which is worth investigating. Multimodality clustering, which seeks a partition of the data in multiple views, has attracted considerable attention, for example, robust multiview spectral clustering (RMSC) explicitly handles the possible noise in the transition probability matrices associated with different views. Spectral clustering algorithm embeds the input data into a low-dimensional representation by dividing the clustering problem into k subproblems, and the corresponding eigenvalue reflects the loss of each subproblem. So, the eigenvalues of the Laplacian matrix should be treated differently, while RMSC regularizes each singular value equally when recovering the low-rank matrix. In this paper, we propose a multimodality clustering algorithm which recovers the low-rank matrix by weighted nuclear norm minimization. We also propose a method to evaluate the weight vector by learning a shared low-rank matrix. In our experiments, we use several real-world datasets to test our method, and experimental results show that the proposed method has a better performance than other baselines.