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

2010 ◽  
Author(s):  
Lara G. Villanueva ◽  
Gustavo M. Callico ◽  
Felix Tobajas ◽  
Sebastian Lopez ◽  
Valentin De Armas ◽  
...  
Keyword(s):  
Image Quality ◽  
Medical Diagnosis ◽  
Super Resolution ◽  
Quality Enhancement ◽  
Image Quality Enhancement

scholarly journals Intra-operative OCT (iOCT) Image Quality Enhancement: A Super-Resolution Approach Using High Quality iOCT 3D Scans

2021 ◽  
pp. 21-31
Author(s):  
Charalampos Komninos ◽  
Theodoros Pissas ◽  
Blanca Flores ◽  
Edward Bloch ◽  
Tom Vercauteren ◽  
...  
Keyword(s):  
Image Quality ◽  
Super Resolution ◽  
Quality Enhancement ◽  
High Quality ◽  
Image Quality Enhancement ◽  
3D Scans

Image quality enhancement based on real-time deconvolution and super resolution

2018 ◽  
Author(s):  
Yoan Marin ◽  
Abdelali Douiyek ◽  
Johel Miteran ◽  
Julien Dubois ◽  
Barthélémy Heyrman ◽  
...  
Keyword(s):  
Image Quality ◽  
Real Time ◽  
Super Resolution ◽  
Quality Enhancement ◽  
Image Quality Enhancement

scholarly journals Computed Tomography (CT) Image Quality Enhancement via a Uniform Framework Integrating Noise Estimation and Super-Resolution Networks

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3348 ◽  
Author(s):  
Chi ◽  
Zhang ◽  
Yu ◽  
Wang ◽  
Wu
Keyword(s):  
Computed Tomography ◽  
Image Quality ◽  
Medical Image ◽  
Super Resolution ◽  
Noise Removal ◽  
Low Noise ◽  
Computer Assisted ◽  
Quality Enhancement ◽  
Ct Image ◽  
Image Quality Enhancement

Computed tomography (CT) imaging technology has been widely used to assist medical diagnosis in recent years. However, noise during the process of imaging, and data compression during the process of storage and transmission always interrupt the image quality, resulting in unreliable performance of the post-processing steps in the computer assisted diagnosis system (CADs), such as medical image segmentation, feature extraction, and medical image classification. Since the degradation of medical images typically appears as noise and low-resolution blurring, in this paper, we propose a uniform deep convolutional neural network (DCNN) framework to handle the de-noising and super-resolution of the CT image at the same time. The framework consists of two steps: Firstly, a dense-inception network integrating an inception structure and dense skip connection is proposed to estimate the noise level. The inception structure is used to extract the noise and blurring features with respect to multiple receptive fields, while the dense skip connection can reuse those extracted features and transfer them across the network. Secondly, a modified residual-dense network combined with joint loss is proposed to reconstruct the high-resolution image with low noise. The inception block is applied on each skip connection of the dense-residual network so that the structure features of the image are transferred through the network more than the noise and blurring features. Moreover, both the perceptual loss and the mean square error (MSE) loss are used to restrain the network, leading to better performance in the reconstruction of image edges and details. Our proposed network integrates the degradation estimation, noise removal, and image super-resolution in one uniform framework to enhance medical image quality. We apply our method to the Cancer Imaging Archive (TCIA) public dataset to evaluate its ability in medical image quality enhancement. The experimental results demonstrate that the proposed method outperforms the state-of-the-art methods on de-noising and super-resolution by providing higher peak signal to noise ratio (PSNR) and structure similarity index (SSIM) values.

Interactive Image Quality Enhancement Support System by Tone Curve Correction Based on Human KANSEI

2009 ◽  
Vol 129 (6) ◽  
pp. 593-600 ◽  
Author(s):  
Yuichiro Tokuda ◽  
Gosuke Ohashi ◽  
Masato Tsukada ◽  
Reiichi Kobayashi ◽  
Yoshifumi Shimodaira
Keyword(s):  
Image Quality ◽  
Support System ◽  
Quality Enhancement ◽  
Curve Correction ◽  
Image Quality Enhancement

Deep Learning Approaches for Whiteboard Image Quality Enhancement

2019 ◽  
Vol 2019 (1) ◽  
pp. 360-368
Author(s):  
Mekides Assefa Abebe ◽  
Jon Yngve Hardeberg
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Image Data ◽  
Quality Enhancement ◽  
Network Architectures ◽  
Learning Approaches ◽  
Data Set ◽  
Image Quality Enhancement ◽  
Processing Techniques ◽  
White Balancing

Different whiteboard image degradations highly reduce the legibility of pen-stroke content as well as the overall quality of the images. Consequently, different researchers addressed the problem through different image enhancement techniques. Most of the state-of-the-art approaches applied common image processing techniques such as background foreground segmentation, text extraction, contrast and color enhancements and white balancing. However, such types of conventional enhancement methods are incapable of recovering severely degraded pen-stroke contents and produce artifacts in the presence of complex pen-stroke illustrations. In order to surmount such problems, the authors have proposed a deep learning based solution. They have contributed a new whiteboard image data set and adopted two deep convolutional neural network architectures for whiteboard image quality enhancement applications. Their different evaluations of the trained models demonstrated their superior performances over the conventional methods.

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