Region-based weighted-norm with adaptive regularization for resolution enhancement

In this paper, we propose a high-resolution image reconstruction algorithm considering inaccurate subpixel registration. A regularized iterative reconstruction algorithm is adopted to overcome the ill-posedness problem resulting from inaccurate subpixel registration. In particular, we use multichannel image reconstruction algorithms suitable for applications with multiframe environments. Since the registration error in each low-resolution image has a different pattern, the regularization parameters are determined adaptively for each channel. We propose two methods for estimating the regularization parameter automatically. The proposed algorithms are robust against registration error noise, and they do not require any prior information about the original image or the registration error process. Information needed to determine the regularization parameter and to reconstruct the image is updated at each iteration step based on the available partially reconstructed image. Experimental results indicate that the proposed algorithms outperform conventional approaches in terms of both objective measurements and visual evaluation.

Regularized Adaptive High-resolution Image Reconstruction Considering Inaccurate Subpixel Registration

Super-resolution (SR) for video sequences is a technique to obtain a higher resolution image by fusing multiple low-resolution (LR) frames of the same scene. In a typical super-resolution algorithm, image registration is one of the most affective steps. The difficulty of this step results in the fact that most of the existing SR algorithms can not cope with local motions because they assume global motion. In this paper, we propose a SR algorithm that takes into account inaccurate estimates of the registration parameters and the point spread function. When frames obey the assumed global motion model, these inaccurate estimates, along with the additive Gaussian noise in the low-resolution image sequence, result in different noise level for each frame. However, in case of existence of local motion and/or occlusion, regions that have local motion and/or occlusion have different noise level. To cope with this problem, we propose to adaptively weight each segment according to its reliability. The segments are generated by segmenting the reference frame using watershed segmentation. The experimental results using real video sequences show the effectiveness of the proposed algorithm compared to three state-of-the-art SR algorithms.

/pdf/region-based-super-resolution-for-video-sequences-6e3ali1mi7.pdf

Region-Based Super Resolution for Video Sequences Considering Registration Error

A kurtosis-based super-resolution image reconstruction algorithm is proposed in this paper. Firstly, we give the definition of the kurtosis image and analyze its two properties: (i) the kurtosis image is Gaussian noise invariant, and (ii) the absolute value of a kurtosis image becomes smaller as the the image gets smoother. Then we build a constrained absolute local kurtosis maximization function to estimate the high-resolution image by fusing multiple blurred low-resolution images corrupted by intensive white Gaussian noise. The Lagrange multiplier is used to solve the combinatorial optimization problem. Experimental results demonstrate that the proposed method is better than the conventional algorithms in terms of visual inspection and robustness, using both synthetic and real world examples under severe noise background. It has an improvement of 0.5 to 2.0 dB in PSNR over other approaches.

Kurtosis-based super-resolution algorithm

This paper addresses the super-resolution image reconstruction problem with the aim to produce a higher-resolution image based on its low-resolution counterparts. The proposed approach adaptively adjusts the degree of regularization using the saliency measure of the local content of the image. This is in contrast to that a spatially-invariant regularization is used for the whole image in conventional approaches. Furthermore, a gradient-based assessment criterion is proposed to measure the saliency of the image. Experiments are conducted to demonstrate the superior performance of the proposed approach.

Spatially-adaptive regularized super-resolution image reconstruction using a gradient-based saliency measure

In this paper a novel high-order statistics (HOS) based regularized algorithm for image super-resolution reconstruction is proposed. In this method, the image is divided into various regions according to the local forth order statistics. The segmentation label is then used to determine the weighted operator of the regularization term. In this way, different regularization terms are applied depending on local characteristics and structures of the image. The proposed image achieves anisotropic diffusion for edge pixels and isotropic diffusion for flat pixels. Experimental results demonstrate that the proposed method performs better than the conventional methods and has high PSNR and MSSIM with sharper edges.

HOS-based image super-resolution reconstruction

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Ju Liu

Author Tools

Chat about Author

Papers

HOS-based image super-resolution reconstruction