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Abstract
In this paper, first, we carried out dictionary learning to process the sparse coding in advance, and then, we added six types of noise for 3D CG images. Next, we processed noise removal based on sparse coding theory and dictionary learning. Before and after image processing, we discussed improvement of image quality evaluation value eventually by measuring PSNR.
An efficient and flexible dictionary structure is proposed for sparse and redundant signal representation. The proposed sparse dictionary is based on a sparsity model of the dictionary atoms over a base dictionary, and takes the form D = ?? A, where ?? is a fixed base dictionary and A is sparse. The sparse dictionary provides efficient forward and adjoint operators, has a compact representation, and can be effectively trained from given example data. In this, the sparse structure bridges the gap between implicit dictionaries, which have efficient implementations yet lack adaptability, and explicit dictionaries, which are fully adaptable but non-efficient and costly to deploy. In this paper, we discuss the advantages of sparse dictionaries, and present an efficient algorithm for training them. We demonstrate the advantages of the proposed structure for 3-D image denoising.
Breast cancer is found to be the most pervasive type of cancer among women. Computer aided detection and diagnosis of cancer at the initial stages can increase the chances of recovery and thus reduce the mortality rate through timely prognosis and adequate treatment planning. The Nuclear Atypia Scoring or histopathological breast tumor grading remains to be a challenging problem due to the various artifacts and variabilities introduced during slide preparation and also because of the complexity in the structure of the underlying tissue patterns. Inspired by the success of Symmetric Positive Definite (SPD) matrices in many of the challenging tasks in machine learning and computer vision, a sparse coding and dictionary learning on SPD matrices is proposed in this paper for the breast tumor grading. The proposed covariance based SPD matrices form a Riemannian manifold and are represented as the sparse combination of Riemannian dictionary atoms. Non-linearity of the SPD manifold is tackled by embedding into the Reproducing Kernel Hilbert Space (RKHS) using kernels derived from log- Euclidean metric, Jeffrey and Stein divergences and compared with the non-kernel based Affine Invariant Riemannian Metric. The novelty of the work lies in exploiting the kernel approach for the Hilbert space embedding of the Riemannian manifold, that can achieve a better discrimination of the breast cancer tissues, following a sparse representation over learned dictionaries and henceforth it outperforms many of the state-of-the-art algorithms in breast cancer grading in terms of quantitative and qualitative analysis.
In recent years, approaches based on nonlocal self-similarity and global structure regularization have led to significant improvements in image restoration. Nonlocal self-similarity exploits the repetitiveness of small image patches as a powerful prior in the reconstruction process. Likewise, global structure regularization is based on the principle that the structure of objects in the image is represented by a relatively small portion of pixels. Enforcing this structural information to be sparse can thus reduce the occurrence of reconstruction artifacts. So far, most image restoration approaches have considered one of these two strategies, but not both. This paper presents a novel image restoration method that combines nonlocal self-similarity and global structure sparsity in a single efficient model. Group of similar patches are reconstructed simultaneously, via an adaptive regularization technique based on the weighted nuclear norm. Moreover, global structure is preserved using an innovative strategy, which decomposes the image into a smooth component and a sparse residual, the latter regularized using
norm. An optimization technique, based on the alternating direction method of multipliers algorithm, is used to recover corrupted images efficiently. The performance of the proposed method is evaluated on two important image restoration tasks: image completion and super-resolution. Experimental results show our method to outperform state-of-the-art approaches for these tasks, for various types and levels of image corruption.
We can come to approach on super-resolution processing based on deep learning by appearing deep learning tools. These performance are shown by applying the only deep learning theory for super-resolution processing. However, we consider that the optimal condition and design for super-resolution processing are achieved better by improving these algorithms and setting parameter appropriately. In this paper, first, we carried out experiments on optimal condition and design of super-resolution processing for the multi-view 3D images encoded and decoded by H.265/HEVC, focused on structure of convolutional neural network by using Chainer. And then, we assessed for the generated images quality objectively, and compare to each image. Finally, we discussed for experimental results.
We can come to approach on super-resolution processing based on deep learning by appearing deep learning tools. These performance are shown by applying the only deep learning theory for super-resolution processing. However, we consider that the optimal condition and design for super-resolution processing are achieved better by improving these algorithms and setting parameter appropriately. In this paper, first, we carried out experiments on optimal condition and design of super-resolution processing for the multi-view 3D images encoded and decoded by H.265/HEVC, focused on structure of convolutional neural network by using Chainer. And then, we assessed for the generated images quality objectively, and compare to each image. Finally, we discussed for experimental results.
Previously, it is not obvious to what extent was accepted for the assessors when we see the 3D image (including multi-view 3D) which the luminance change may affect the stereoscopic effect and assessment generally. We think that we can conduct a general evaluation, along with a subjective evaluation, of the luminance component using both the S-CIELAB color space and CIEDE2000. In this study, first, we performed three types of subjective evaluation experiments for contrast enhancement in an image by using the eight viewpoints parallax barrier method. Next, we analyzed the results statistically by using a support vector machine (SVM). Further, we objectively evaluated the luminance value measurement by using CIEDE2000 in the S-CIELAB color space. Then, we checked whether the objective evaluation value was related to the subjective evaluation value. From results, we were able to see the characteristic relationship between subjective assessment and objective assessment.
We address the image denoising problem, where zero-mean white and homogeneous Gaussian additive noise is to be removed from a given image. The approach taken is based on sparse and redundant representations over trained dictionaries. Using the K-SVD algorithm, we obtain a dictionary that describes the image content effectively. Two training options are considered: using the corrupted image itself, or training on a corpus of high-quality image database. Since the K-SVD is limited in handling small image patches, we extend its deployment to arbitrary image sizes by defining a global image prior that forces sparsity over patches in every location in the image. We show how such Bayesian treatment leads to a simple and effective denoising algorithm. This leads to a state-of-the-art denoising performance, equivalent and sometimes surpassing recently published leading alternative denoising methods.