Handling noise in textual image resolution enhancement using online and offline learned dictionaries

Walha, Rim; Drira, Fadoua; Lebourgeois, Frank; García, Christophe; Alimi, Adel M.

doi:10.1007/s10032-017-0294-6

Cited by 9 publications

(1 citation statement)

References 69 publications

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“…This method improves the robustness of texture details while avoiding gradient inversion artifacts that may appear in the fused image after DL enhancement. In [12], the author conducted extensive experiments on synthetic and real low-resolution noisy text images, and verified the effectiveness of the proposed system visually and quantitatively. Compared with the most advanced methods, encouraging results can be achieved in image visual quality and character recognition rate [13].…”

Section: Introductionmentioning

confidence: 90%

Multi-Feature Fusion Algorithm in VR Panoramic Image Detail Enhancement Processing

Zhu

2020

IEEE Access

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VR panoramic image is an image imaging technology that covers a wide range of scenes. Its imaging range is much larger than that of traditional imaging systems, and it can fully reflect all the information of the imaging space. However, the current VR panoramic images have the problems that the details are not obvious enough and the processing is not comprehensive enough. In view of the shadow problem in VR panoramic images, this paper proposes a multi-feature fusion VR panoramic image shadow elimination algorithm, which uses HSV color features and LBP / LSFP texture features to obtain shadow detection results, and then obtains the final detection results by fusion. The experimental results prove that while ensuring a low missed detection rate, the false detection rate is greatly reduced. The comprehensive evaluation index Avg in this paper is improved by 3.4% compared with the shadow elimination algorithm based on a single feature. This paper proposes an image saliency detection algorithm and image detail enhancement algorithm based on multi-feature fusion. The final saliency map is obtained through linear fusion. Experiments prove that the image detail enhancement algorithm based on multi-feature fusion mentioned in this paper has achieved very good results. This paper compares the performance of single-feature and multi-feature fusion algorithms. HSV, LBP and LSFP obtain 93.39% accuracy and 0.8708 correlation coefficient. The effect of multi-fusion fusion is better than that of single feature.

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Section: Introductionmentioning

confidence: 90%