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 Effective Analysis of an Iterative Median Filter-Hardware and Software Perspective

2019 ◽  
Vol 8 (3) ◽  
pp. 5580-5583
Keyword(s):  
Image Quality ◽  
Real Time ◽  
Median Filter ◽  
Iteration Process ◽  
Software Implementation ◽  
Quality Enhancement ◽  
Image Quality Enhancement ◽  
Key Factor ◽  
Effective Analysis

Image quality enhancement is a very predominant domain of discussion as the complexity of system increases, the software implementation becomes a key factor but it is not always reliable to use the approach for adaptable applications such as medical, military or real time purpose. In order to address such scenarios it is necessary to have a reconfigurable and an adaptable implementation. In this paper we have addressed the hardware modeling of a median filter using double iteration process. The iterative median filter is implemented on an Artix-7 FPGA.

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

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
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