A depth-based super-resolution method for multi-view color images

2016 ◽  
Author(s):  
Jun Fan ◽  
Xiangrong Zeng ◽  
Qizi Huangpeng ◽  
Jinglun Zhou ◽  
Jing Feng
Keyword(s):  
Super Resolution ◽  
Color Images ◽  
Resolution Method

An Efficient Super Resolution Based on Image Dimensionality Reduction Using Accumulative Intensity Gradient

2014 ◽  
Vol 18 (4) ◽  
pp. 518-528 ◽  
Author(s):  
Muhammad Haris ◽  
◽  
Kazuhito Sawase ◽  
Muhammad Rahmat Widyanto ◽  
Hajime Nobuhara ◽  
...  
Keyword(s):  
Color Image ◽  
Super Resolution ◽  
Computation Time ◽  
Color Images ◽  
Natural Images ◽  
Image Generation ◽  
Intensity Gradient ◽  
Resolution Method ◽  
Edge Direction ◽  
Resolution Algorithm

An efficient super resolution algorithm based on edge direction is proposed based on the dimensional reduction of color images and 3 types of edge direction. The basic idea is to reduce image – especially color image – dimensions and to interpolate pixels by using 3 simple edge directions – vertical, horizontal, and diagonal. The proposed algorithm conceivably eliminate more color artifacts than Bicubic. The results of experiments using 30 natural images confirm that PSNR of the proposed method achieve the same quality as Fast Curvature Based Interpolation (FCBI). We confirmed that computation time for the proposed method was 40% shorter for RGB and 20% shorter for grayscale images than the previous fastest method, i.e., FCBI. We show efficient panorama image generation based on the proposed super resolution method as one application of our proposal.

scholarly journals Multiframe Super-Resolution of Color Images Based on Cross Channel Prior

Symmetry ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 901
Author(s):  
Shen Shi ◽  
Bing Xiangli ◽  
Zengshan Yin
Keyword(s):  
Color Image ◽  
Super Resolution ◽  
Color Images ◽  
Experimental Results ◽  
Reconstruction Method ◽  
Resolution Method ◽  
The Cross ◽  
Color Channels

Color images have a wider range of applications than gray images. There are two ways to extend the traditional super-resolution reconstruction method to color images: Super resolution reconstructs each channel of the color image individually; Change the RGB color bands into YCrCb color bands, then super-resolution reconstructs the luminance component and interpolates the chrominance components.These algorithms cannot effectively utilize the property that the edges and textures are similar in the RGB channels, and the results of those methods may lead to color artifacts. Aiming to solve these problems, we propose a new super-resolution method based on cross channel prior. First, a cross channel prior is proposed to describe the similarity of gradient in RGB channels. Then, a new super-resolution method is proposed for color images via combination of the cross channel prior and the traditional super-resolution methods. Finally, the proposed method reconstructs the color channels alternately. The experimental results show that the proposed method could effectively suppress the generation of color artifacts and improve the quality of the reconstructed images.

Study of advanced mask inspection optics with super-resolution method for next-generation mask fabrication

2009 ◽  
Author(s):  
Ryoichi Hirano ◽  
Masatoshi Hirono ◽  
Riki Ogawa ◽  
Nobutaka Kikuiri ◽  
Kenichi Takahara ◽  
...  
Keyword(s):  
Super Resolution ◽  
Next Generation ◽  
Resolution Method ◽  
Mask Inspection ◽  
Mask Fabrication

scholarly journals Implementasi Metode Super Resolusi Untuk Meningkatkan Kualitas Citra Hasil Screenshot

2020 ◽  
Vol 7 (3) ◽  
pp. 432
Author(s):  
Windi Astuti
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Median Filter ◽  
Super Resolution ◽  
Linear Interpolation ◽  
Resolution Method ◽  
Image Zooming ◽  
Image Enlargement ◽  
Three Stages

Various types of image processing that can be done by computers, such as improving image quality is one of the fields that is quite popular until now. Improving the quality of an image is necessary so that someone can observe the image clearly and in detail without any disturbance. An image can experience major disturbances or errors in an image such as the image of the screenshot is used as a sample. The results of the image from the screenshot have the smallest sharpness and smoothness of the image, so to get a better image is usually done enlargement of the image. After the screenshot results are obtained then, the next process is cropping the image and the image looks like there are disturbances such as visible blur and cracked. To get an enlarged image (Zooming image) by adding new pixels or points. This is done by the super resolution method, super resolution has three stages of completion, first Registration, Interpolation, and Reconstruction. For magnification done by linear interpolation and reconstruction using a median filter for image refinement. This method is expected to be able to solve the problem of improving image quality in image enlargement applications. This study discusses that the process carried out to implement image enlargement based on the super resolution method is then built by using R2013a matlab as an editor to edit programs

scholarly journals A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

2021 ◽  
Vol 13 (20) ◽  
pp. 4115
Author(s):  
Ke Tan ◽  
Xingyu Lu ◽  
Jianchao Yang ◽  
Weimin Su ◽  
Hong Gu
Keyword(s):  
Random Field ◽  
Objective Function ◽  
Markov Random Field ◽  
Prior Information ◽  
Structural Characteristics ◽  
Super Resolution ◽  
Resolution Method ◽  
Markov Random ◽  
Shrinkage Method ◽  
Forward Looking

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

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