Medical Diagnosis Improvement Through Image Quality Enhancement Based on Super-Resolution

Villanueva, Lara González; Callicó, Gustavo M.; Tobajas, F.; López, Sebastián; Armas, V. de; Lopez, J.F.; Sarmiento, Roberto

doi:10.1109/dsd.2010.35

Cited by 3 publications

(4 citation statements)

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“…Reference [5] presented a color image enhancement method using daubechies wavelet transform and HIS color space. Reference [6] enhanced the pathological anatomy images based on superresolution to improve medical diagnosis. In recent years, the Retinex theory is widely used in the medical image processing [7].…”

Section: Introductionmentioning

confidence: 99%

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

Sun

Jiang

Zhu

et al. 2016

BioMed Research International

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Pathological image enhancement is a significant topic in the field of pathological image processing. This paper proposes a high dynamic range (HDR) pathological image enhancement method based on improved bias field correction and guided image filter (GIF). Firstly, a preprocessing including stain normalization and wavelet denoising is performed for Haematoxylin and Eosin (H and E) stained pathological image. Then, an improved bias field correction model is developed to enhance the influence of light for high-frequency part in image and correct the intensity inhomogeneity and detail discontinuity of image. Next, HDR pathological image is generated based on least square method using low dynamic range (LDR) image, H and E channel images. Finally, the fine enhanced image is acquired after the detail enhancement process. Experiments with 140 pathological images demonstrate the performance advantages of our proposed method as compared with related work.

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Section: Introductionmentioning

confidence: 99%

HDR Pathological Image Enhancement Based on Improved Bias Field Correction and Guided Image Filter

Sun

Jiang

Zhu

et al. 2016

BioMed Research International

View full text Add to dashboard Cite

show abstract

“…Pr Pr W DB (11) The last term A{X} represents the regularization term and λ represents the regularization parameter. Therefore, the resolution enhancement problem can be described as to minimize where .…”

Section: Resolution Enhancement Problem Descriptionmentioning

confidence: 99%

“…Digital image processing methods to obtain a HR image from LR observations have been investigated by many researchers [11][12][13][14]. In [11][12][13], the resolution enhancement are done as a post-processing step, which increases the computational time, while the resolution enhancement in [14] is done with the reconstruction step alternatively, where the resolution enhancement is done in the Sinogram space by projecting the HR image in the Sinogram space. In [14], the quality of BP reconstruction is improved by iteratively reconstructing the CT image and super-resolve the reconstructed image.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the conventional approaches [11][12][13] that are done as a post-process; we propose to solve the resolution enhancement problem simultaneously with the reconstruction problem in the Sinogram space using iterative technique. Unlike the work done in [14], which used projection of HR in the Sinogram space, the proposed approach does not use analytical projection for the reconstruction step.…”

Section: Introductionmentioning

confidence: 99%

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