Diffuse optical tomogram restoration with spatially variant point spread function

2006 ◽  
Author(s):  
Alexander B. Konovalov
Keyword(s):  
Point Spread Function ◽  
Optical Tomogram ◽  
Point Spread ◽  
Spread Function ◽  
Spatially Variant

scholarly journals Convolutional Neural Network with Spatial-Variant Convolution Kernel

2020 ◽  
Vol 12 (17) ◽  
pp. 2811
Author(s):  
Yongpeng Dai ◽  
Tian Jin ◽  
Yongkun Song ◽  
Shilong Sun ◽  
Chen Wu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Enhancement ◽  
Point Spread Function ◽  
Radar Image ◽  
Radar Images ◽  
Point Spread ◽  
Spread Function ◽  
Spatially Variant ◽  
The Impact

Radar images suffer from the impact of sidelobes. Several sidelobe-suppressing methods including the convolutional neural network (CNN)-based one has been proposed. However, the point spread function (PSF) in the radar images is sometimes spatially variant and affects the performance of the CNN. We propose the spatial-variant convolutional neural network (SV-CNN) aimed at this problem. It will also perform well in other conditions when there are spatially variant features. The convolutional kernels of the CNN can detect motifs with some distinctive features and are invariant to the local position of the motifs. This makes the convolutional neural networks widely used in image processing fields such as image recognition, handwriting recognition, image super-resolution, and semantic segmentation. They also perform well in radar image enhancement. However, the local position invariant character might not be good for radar image enhancement, when features of motifs (also known as the point spread function in the radar imaging field) vary with the positions. In this paper, we proposed an SV-CNN with spatial-variant convolution kernels (SV-CK). Its function is illustrated through a special application of enhancing the radar images. After being trained using radar images with position-codings as the samples, the SV-CNN can enhance the radar images. Because the SV-CNN reads information of the local position contained in the position-coding, it performs better than the conventional CNN. The advance of the proposed SV-CNN is tested using both simulated and real radar images.

Parallel image restoration with spatially variant point spread function: description and first clinical results

2001 ◽  
Author(s):  
Andrew Shearer ◽  
Gerard Gorman ◽  
Triona O'Doherty ◽  
Wilhelm J. van der Putten ◽  
Peter McCarthy ◽  
...  
Keyword(s):  
Image Restoration ◽  
Point Spread Function ◽  
Clinical Results ◽  
Point Spread ◽  
Parallel Image ◽  
Spread Function ◽  
Spatially Variant
Sign in / Sign up

Export Citation Format

Share Document