12,027 research outputs found
Rethinking the Pipeline of Demosaicing, Denoising and Super-Resolution
Incomplete color sampling, noise degradation, and limited resolution are the
three key problems that are unavoidable in modern camera systems. Demosaicing
(DM), denoising (DN), and super-resolution (SR) are core components in a
digital image processing pipeline to overcome the three problems above,
respectively. Although each of these problems has been studied actively, the
mixture problem of DM, DN, and SR, which is a higher practical value, lacks
enough attention. Such a mixture problem is usually solved by a sequential
solution (applying each method independently in a fixed order: DM DN
SR), or is simply tackled by an end-to-end network without enough
analysis into interactions among tasks, resulting in an undesired performance
drop in the final image quality. In this paper, we rethink the mixture problem
from a holistic perspective and propose a new image processing pipeline: DN
SR DM. Extensive experiments show that simply modifying the usual
sequential solution by leveraging our proposed pipeline could enhance the image
quality by a large margin. We further adopt the proposed pipeline into an
end-to-end network, and present Trinity Enhancement Network (TENet).
Quantitative and qualitative experiments demonstrate the superiority of our
TENet to the state-of-the-art. Besides, we notice the literature lacks a full
color sampled dataset. To this end, we contribute a new high-quality full color
sampled real-world dataset, namely PixelShift200. Our experiments show the
benefit of the proposed PixelShift200 dataset for raw image processing.Comment: Code is available at: https://github.com/guochengqian/TENe
How Does the Low-Rank Matrix Decomposition Help Internal and External Learnings for Super-Resolution
Wisely utilizing the internal and external learning methods is a new
challenge in super-resolution problem. To address this issue, we analyze the
attributes of two methodologies and find two observations of their recovered
details: 1) they are complementary in both feature space and image plane, 2)
they distribute sparsely in the spatial space. These inspire us to propose a
low-rank solution which effectively integrates two learning methods and then
achieves a superior result. To fit this solution, the internal learning method
and the external learning method are tailored to produce multiple preliminary
results. Our theoretical analysis and experiment prove that the proposed
low-rank solution does not require massive inputs to guarantee the performance,
and thereby simplifying the design of two learning methods for the solution.
Intensive experiments show the proposed solution improves the single learning
method in both qualitative and quantitative assessments. Surprisingly, it shows
more superior capability on noisy images and outperforms state-of-the-art
methods
Single Frame Image super Resolution using Learned Directionlets
In this paper, a new directionally adaptive, learning based, single image
super resolution method using multiple direction wavelet transform, called
Directionlets is presented. This method uses directionlets to effectively
capture directional features and to extract edge information along different
directions of a set of available high resolution images .This information is
used as the training set for super resolving a low resolution input image and
the Directionlet coefficients at finer scales of its high-resolution image are
learned locally from this training set and the inverse Directionlet transform
recovers the super-resolved high resolution image. The simulation results
showed that the proposed approach outperforms standard interpolation techniques
like Cubic spline interpolation as well as standard Wavelet-based learning,
both visually and in terms of the mean squared error (mse) values. This method
gives good result with aliased images also.Comment: 14 pages,6 figure
Combining Super Resolution Algorithm (Gaussian Denoising and Kernel Blurring) and Compare with Camera Super Resolution
This problem addresses the problem of low-resolution image (noisy) that will proof later by PSNR number. The best way to improve this low-resolution problem is by utilizing Super Resolution (SR) algorithm methodology. SR algorithm methodology refers to the process of obtaining higher-resolution images from several lower-resolution ones, that is resolution enhancement. The quality improvement is caused by fractional-pixel displacements between images. SR allows overcoming the limitations of the imaging system (resolving limit of the sensors) without the need for additional hardware. This research aims to find the best SR algorithm in form of stand-alone algorithm or combine algorithm by comparing with the latest SR algorithm (Camera SR) from the previous research made by Chang Chen et al in 2019. Furthermore, we confidence this research will become the future guideline for anyone who want to improve the limitation of their low-resolution camera or vision sensor by implementing those SR algorithms
- …