scholarly journals Resolution enhancement of low resolution wavefields with POCS algorithm

2003 ◽  
Vol 39 (25) ◽  
pp. 1808 ◽  
Author(s):  
A.E. Çetin ◽  
H. Özaktaş ◽  
H.M. Ozaktas
Keyword(s):  
Resolution Enhancement ◽  
Low Resolution

OBJECT QUALITY ENHANCEMENT OF MULTI-FRAME LOW-RESOLUTION VIDEO

2015 ◽  
Vol 77 (22) ◽  
Author(s):  
Siti Aisyah ◽  
Fitri Arnia
Keyword(s):  
Nearest Neighbor ◽  
Resolution Enhancement ◽  
Subjective Assessment ◽  
Object Identification ◽  
Image Resolution ◽  
Frame Rate ◽  
Low Resolution ◽  
Video Frames ◽  
Enhancement Method ◽  
Good Quality Image

A good quality image is required in various applications such as object identification and authentication. This research presents the performance of image resolution enhancement method, in which the low-resolution image originated from low-resolution CCTV video. The enhancement method is initialized by averaging video frames and continued by interpolating the resulted images using the existing interpolation techniques, namely bilinear, bi-cubic, nearest neighbor and spleen. Frame rate of 15 and 25 frames per second (fps) has been applied to the testing video. The result shows that the differences of frame rate and number of the averaged frame would affect image quality. Subjective assessment of respondents of MOS above 3 has been obtained by increasing the frame rate and the number of averaging frames.  

Super-resolution enhancement technique for low resolution video

2010 ◽  
Vol 56 (2) ◽  
pp. 919-924 ◽  
Author(s):  
Mohammad Islam ◽  
Vijayan Asari ◽  
Mohammed Islam ◽  
Mohammad Karim
Keyword(s):  
Resolution Enhancement ◽  
Super Resolution ◽  
Low Resolution ◽  
Enhancement Technique ◽  
Resolution Enhancement Technique

scholarly journals Super-Resolution Enhancement Method Based on Generative Adversarial Network for Integral Imaging Microscopy

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2164
Author(s):  
Md. Shahinur Alam ◽  
Ki-Chul Kwon ◽  
Munkh-Uchral Erdenebat ◽  
Mohammed Y. Abbass ◽  
Md. Ashraful Alam ◽  
...  
Keyword(s):  
High Resolution ◽  
Resolution Enhancement ◽  
Three Dimensional ◽  
Super Resolution ◽  
Input Image ◽  
Low Resolution ◽  
Generative Adversarial Network ◽  
Microscopic Object ◽  
Integral Imaging ◽  
Adversarial Network

The integral imaging microscopy system provides a three-dimensional visualization of a microscopic object. However, it has a low-resolution problem due to the fundamental limitation of the F-number (the aperture stops) by using micro lens array (MLA) and a poor illumination environment. In this paper, a generative adversarial network (GAN)-based super-resolution algorithm is proposed to enhance the resolution where the directional view image is directly fed as input. In a GAN network, the generator regresses the high-resolution output from the low-resolution input image, whereas the discriminator distinguishes between the original and generated image. In the generator part, we use consecutive residual blocks with the content loss to retrieve the photo-realistic original image. It can restore the edges and enhance the resolution by ×2, ×4, and even ×8 times without seriously hampering the image quality. The model is tested with a variety of low-resolution microscopic sample images and successfully generates high-resolution directional view images with better illumination. The quantitative analysis shows that the proposed model performs better for microscopic images than the existing algorithms.

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