A novel adaptive de-blocking algorithm for the smoothlets reconstructed image

Author(s):  
Fang Xie ◽  
Chang Duan ◽  
Jianshu Cao
2020 ◽  
Vol 28 (6) ◽  
pp. 829-847
Author(s):  
Hua Huang ◽  
Chengwu Lu ◽  
Lingli Zhang ◽  
Weiwei Wang

AbstractThe projection data obtained using the computed tomography (CT) technique are often incomplete and inconsistent owing to the radiation exposure and practical environment of the CT process, which may lead to a few-view reconstruction problem. Reconstructing an object from few projection views is often an ill-posed inverse problem. To solve such problems, regularization is an effective technique, in which the ill-posed problem is approximated considering a family of neighboring well-posed problems. In this study, we considered the {\ell_{1/2}} regularization to solve such ill-posed problems. Subsequently, the half thresholding algorithm was employed to solve the {\ell_{1/2}} regularization-based problem. The convergence analysis of the proposed method was performed, and the error bound between the reference image and reconstructed image was clarified. Finally, the stability of the proposed method was analyzed. The result of numerical experiments demonstrated that the proposed method can outperform the classical reconstruction algorithms in terms of noise suppression and preserving the details of the reconstructed image.


2021 ◽  
Vol 40 (3) ◽  
pp. 233-247 ◽  
Author(s):  
Alicja Relidzyńska

Expressions of nostalgia for the 1980s in contemporary American culture are diverse. The most interesting of them go beyond a wistful longing for the past. A complex ‘nostalgia trip’ offered by Netflix’s Stranger Things serves as a notable case study of a distinctive type of this sentiment. Instead of yearning for the restoration of previous times, it plays with past aesthetics in a critically articulate manner, effectively demythologizing the depicted decade. I argue that this significant alteration of the traditional sentiment stems largely from the recent acknowledgment of the Anthropocene and its irreversibility. This article aims to examine the peculiar, self-aware, paradoxical nostalgia, which is coloured by the current, Anthropocene-induced fears for the environment and, thus, our future. The analysis of Stranger Things – its thematics, genre, visuals and the meticulously reconstructed image of the presented era – draws parallels to the techniques employed by the ‘novel nostalgia’: bitter, ironic depiction of the past and references to natural phenomena. The study thus investigates the show at the intersection of contemporary nostalgia for the 1980s and the cultural repercussions of the Anthropocene. In so doing, it will unravel the innovation in the programme’s discourse on the 1980s decade in American culture.


2016 ◽  
Vol 2016 ◽  
pp. 1-9
Author(s):  
Ran Li ◽  
Hongbing Liu ◽  
Yu Zeng ◽  
Yanling Li

In the framework of block Compressed Sensing (CS), the reconstruction algorithm based on the Smoothed Projected Landweber (SPL) iteration can achieve the better rate-distortion performance with a low computational complexity, especially for using the Principle Components Analysis (PCA) to perform the adaptive hard-thresholding shrinkage. However, during learning the PCA matrix, it affects the reconstruction performance of Landweber iteration to neglect the stationary local structural characteristic of image. To solve the above problem, this paper firstly uses the Granular Computing (GrC) to decompose an image into several granules depending on the structural features of patches. Then, we perform the PCA to learn the sparse representation basis corresponding to each granule. Finally, the hard-thresholding shrinkage is employed to remove the noises in patches. The patches in granule have the stationary local structural characteristic, so that our method can effectively improve the performance of hard-thresholding shrinkage. Experimental results indicate that the reconstructed image by the proposed algorithm has better objective quality when compared with several traditional ones. The edge and texture details in the reconstructed image are better preserved, which guarantees the better visual quality. Besides, our method has still a low computational complexity of reconstruction.


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