Adaptive total variation deringing method for image interpolation

Krylov, Andrey S.; Nasonov, Andrey

doi:10.1109/icip.2008.4712328

Cited by 12 publications

(8 citation statements)

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“…Another advantage of the proposed method is that it yields a fairly low computational complexity. Compared with the TV-based method in [11] which requires about 100 iterations to reach convergence, our algorithm can converge in 5 iterations. For the downsampled test image "Cameraman" (with the size of 128 × 128), our algorithm (written in MATLAB) only takes 1.3 seconds (based on 5 iterations) to complete the entire interpolation process.…”

Section: Resultsmentioning

confidence: 91%

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Edge-contrast-guided image interpolation using directional variation field diffusion

Wei

Cai

2010

2010 IEEE International Conference on Image Processing

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In this paper, a novel interpolation method is proposed, called the edge-contrast-guided interpolation (ECGI), which takes the edge contrast into consideration. Similar to the existing edge-guided methods, for those edge pixels under interpolation, the proposed ECGI will conduct the interpolation along their associated edge's direction. However, the novelty of our proposed ECGI lies in the treatment of non-edge pixels: We additionally view those non-edge pixels as 'edge' pixels, if they are locating in the vicinity of true edges. In this case, each 'edge' pixel is subject to conduct edge-guided interpolation along the same direction of the true edge that influences it. In our work, we use the edge contrast to determine how far a true edge could yield such influence on its nearby pixels; consequently, the stronger the contrast, the more pixels in its vicinity will be viewed as 'edge' pixels. To implement this idea, we apply a variational approach over the directional variation (DV) fields to obtain new diffused DV fields for conducting edge-guided interpolation. Compared with the stateof-the-art interpolation methods, our method is able to deliver superior performance, yielding more clear edges without introducing ringing or other artifacts, while enjoying fairly low computational complexity.

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Section: Resultsmentioning

confidence: 91%

“…Extensive simulation experiments were conducted to evaluate the proposed image interpolation technique and compared with four other image interpolation methods: the bicubic interpolation [1], the edge-directed interpolation (EDI) [2], the total variation (TV)-based interpolation [11] and the softdecision adaptive interpolation (SAI) [4]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Edge-contrast-guided image interpolation using directional variation field diffusion

Wei

Cai

2010

2010 IEEE International Conference on Image Processing

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“…Correspondence between the proposed ringing level and existing image deringing algorithm. Ringing level was calculated for the initial low-resolution image, for the image resampled by regularization method [11] and for the resampled image postprocessed by deringing method [5]. The results are given in Fig.6.…”

Section: Resultsmentioning

confidence: 99%

“…Regularization parameter estimation for image deringing using MAP approach is proposed in [4]. For the problem of image deringing after resampling, regularization parameter is estimated using information on the initial low resolution image [5]. In [6], the ringing metrics is defined as maximum of the differences between pixel values of the reference image and the processed image in the edge neighborhood.…”

Section: Introductionmentioning

confidence: 99%

Scale-space method of image ringing estimation

Nasonov

Krylov

2009

2009 16th IEEE International Conference on Image Processing (ICIP)

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“…Deringing: Existing approaches include iterative projection onto convex sets (POCS) [17], total variation [18][19][20], anisotropy [21], bilateral filtering and its variants [22][23][24][25] and quadtree decompositions [26][27][28].…”

Section: Introductionmentioning

confidence: 99%

Perceptually optimized blind repair of natural images

Moorthy

Mittal

Bovik

2013

Signal Processing: Image Communication

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This article appeared in a journal published by Elsevier. The attached copy is furnished to the author for internal non-commercial research and education use, including for instruction at the authors institution and sharing with colleagues.Other uses, including reproduction and distribution, or selling or licensing copies, or posting to personal, institutional or third party websites are prohibited. b s t r a c tWe define the new idea of blind image repair as a process of correcting one or more different and unknown types of distortions afflicting an image. These distortions could introduce linear or non-linear degradations, compression artifacts, noise, etc., or combinations of these. Thus the concept encompasses denoising, deblurring, deblocking, deringing, and other post-acquisition image improvement processes that address distortions. The problem is distortion-blind when the natures of the distortion processes are unknown prior to analyzing the image. Towards solving this problem, we describe a new framework for repairing an image that has undergone an unknown set of distortions, based on identifying the distortion(s) present in the image (if any) and applying possibly multiple distortion-specific image repair algorithms. Our philosophy is based on the principle that the task of general purpose image repair is one of agglomeration, i.e., the algorithm should embody multiple high-performing distortion-specific repair modules such that seamless general purpose image repair is achieved. Our proposed frameworkthe GEneral-purpose No-reference Image Improver (GENII) -enables the design of algorithms that are blind to distortion type as well as to distortion parameters, and only requires as input the distorted image to be repaired. The GENII framework is modular and easily extensible to image repair problems beyond those considered here. GENII operates by using natural scene statistic models to identify distortion, to perceptually optimize the distortion parameter(s), to assess the quality of the intermediate repaired images, and to perceptually optimize the repair processes. We explain the general purpose image repair framework and one specific realization, dubbed GENII-1, which assumes that the image has been affected by one or more of four possible distortion types.The performance of GENII-1 is evaluated on 4000 distorted images, and shown to deliver substantial improvements in both quantitative and qualitative visual quality.

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Adaptive total variation deringing method for image interpolation

Cited by 12 publications

References 12 publications

Edge-contrast-guided image interpolation using directional variation field diffusion

Edge-contrast-guided image interpolation using directional variation field diffusion

Scale-space method of image ringing estimation

Perceptually optimized blind repair of natural images

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