scholarly journals Design of Cascaded CNN for Medical Image Super Resolution

2021 ◽  
Vol 7 (3) ◽  
pp. 22-29
Author(s):  
Kajol Singh ◽  
Manish Saxena
Keyword(s):  
Neural Networks ◽  
Medical Image ◽  
Research Work ◽  
Super Resolution ◽  
Primary Objective ◽  
Lighting Conditions ◽  
Proposed Model ◽  
Filter Size ◽  
Medical Image Reconstruction ◽  
Image Super Resolution

The images captured through a camera usually belong to over or under exposed conditions. The reason may be inappropriate lighting conditions or camera resolution. Hence, it is of utmost importance to have a few enhancement techniques that could make these artefacts look better. Hence, the primary objective pertaining to the adjustment and enhancement techniques is to enhance the characteristics of an image. The initial numeric values related to an image get distorted when an image is enhanced. Therefore, enhancement techniques should be designed in such a way that the image quality isn’t compromised. This research work is focused on proposed a network design for deep convolution neural networks for application of super resolution techniques. To improve the complexity of existing techniques this work is intended towards network designs, different filter size and CNN architecture. The CNN model is most effective model for detection and segmentation in image. This model will improve the efficiency of medical image reconstruction from LR to HR. The proposed model showed its efficiency not only PET medical images but also on retinal database and achieved advance results as compared to existing works.

scholarly journals Semi-Coupled Convolutional Sparse Learning for Image Super-Resolution

2019 ◽  
Vol 11 (21) ◽  
pp. 2593
Author(s):  
Li ◽  
Zhang ◽  
Jiao ◽  
Liu ◽  
Yang ◽  
...  
Keyword(s):  
High Resolution ◽  
Super Resolution ◽  
Mapping Function ◽  
Linear Mapping ◽  
Sparse Learning ◽  
Proposed Model ◽  
Filter Size ◽  
High Resolution Images ◽  
Resolution Problem ◽  
Image Super Resolution

In the convolutional sparse coding-based image super-resolution problem, the coefficients of low- and high-resolution images in the same position are assumed to be equivalent, which enforces an identical structure of low- and high-resolution images. However, in fact the structure of high-resolution images is much more complicated than that of low-resolution images. In order to reduce the coupling between low- and high-resolution representations, a semi-coupled convolutional sparse learning method (SCCSL) is proposed for image super-resolution. The proposed method uses nonlinear convolution operations as the mapping function between low- and high-resolution features, and conventional linear mapping can be seen as a special case of the proposed method. Secondly, the neighborhoods within the filter size are used to calculate the current pixel, improving the flexibility of our proposed model. In addition, the filter size is adjustable. In order to illustrate the effectiveness of SCCSL method, we compare it with four state-of-the-art methods of 15 commonly used images. Experimental results show that this work provides a more flexible and efficient approach for image super-resolution problem.

scholarly journals A weighted least squares optimisation strategy for medical image super resolution via multiscale convolutional neural networks for healthcare applications

2021 ◽  
Author(s):  
Bhawna Goyal ◽  
Dawa Chyophel Lepcha ◽  
Ayush Dogra ◽  
Shui-Hua Wang
Keyword(s):  
Neural Networks ◽  
Least Squares ◽  
Convolutional Neural Networks ◽  
Medical Image ◽  
Medical Images ◽  
Weighted Least Squares ◽  
Super Resolution ◽  
Poor Quality ◽  
Healthcare Applications ◽  
Image Super Resolution

AbstractMedical imaging is an essential medical diagnosis system subsequently integrated with artificial intelligence for assistance in clinical diagnosis. The actual medical images acquired during the image capturing procedures generate poor quality images as a result of numerous physical restrictions of the imaging equipment and time constraints. Recently, medical image super-resolution (SR) has emerged as an indispensable research subject in the community of image processing to address such limitations. SR is a classical computer vision operation that attempts to restore a visually sharp high-resolution images from the degraded low-resolution images. In this study, an effective medical super-resolution approach based on weighted least squares optimisation via multiscale convolutional neural networks (CNNs) has been proposed for lesion localisation. The weighted least squares optimisation strategy that particularly is well-suited for progressively coarsening the original images and simultaneously extract multiscale information has been executed. Subsequently, a SR model by training CNNs based on wavelet analysis has been designed by carrying out wavelet decomposition of optimized images for multiscale representations. Then multiple CNNs have been trained separately to approximate the wavelet multiscale representations. The trained multiple convolutional neural networks characterize medical images in many directions and multiscale frequency bands, and thus facilitate image restoration subject to increased number of variations depicted in different dimensions and orientations. Finally, the trained CNNs regress wavelet multiscale representations from a LR medical images, followed by wavelet synthesis that forms a reconstructed HR medical image. The experimental performance indicates that the proposed model SR restoration approach achieve superior SR efficiency over existing comparative methods

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution

Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network

2020 ◽  
Vol 57 (2) ◽  
pp. 021014
Author(s):  
刘可文 Liu Kewen ◽  
马圆 Ma Yuan ◽  
熊红霞 Xiong Hongxia ◽  
严泽军 Yan Zejun ◽  
周志军 Zhou Zhijun ◽  
...  
Keyword(s):  
Medical Image ◽  
Super Resolution ◽  
Reconstruction Method ◽  
Attention Network ◽  
Image Super Resolution
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