This paper proposes an edge-directed interpolation algorithm for natural images. The basic idea is to first estimate local covariance coefficients from a low-resolution image and then use these covariance estimates to adapt the interpolation at a higher resolution based on the geometric duality between the low-resolution covariance and the high-resolution covariance. The edge-directed property of covariance-based adaptation attributes to its capability of tuning the interpolation coefficients to match an arbitrarily oriented step edge. A hybrid approach of switching between bilinear interpolation and covariance-based adaptive interpolation is proposed to reduce the overall computational complexity. Two important applications of the new interpolation algorithm are studied: resolution enhancement of grayscale images and reconstruction of color images from CCD samples. Simulation results demonstrate that our new interpolation algorithm substantially improves the subjective quality of the interpolated images over conventional linear interpolation.

New edge-directed interpolation

https://dash.harvard.edu/bitstream/handle/1/29005173/Formulation%20of%20Functionalized%20PLGA-PEG.pdf?sequence=1

Formulation of functionalized PLGA-PEG nanoparticles for in vivo targeted drug delivery

This paper introduces a new interpolation technique for demosaicing of color images produced by single-CCD digital cameras. We show that the proposed simple linear filter can lead to an improvement in PSNR of over 5.5 dB when compared to bilinear demosaicing, and about 0.7 dB improvement in R and B interpolation when compared to a recently introduced linear interpolator. The proposed filter also outperforms most nonlinear demosaicing algorithms, without the artifacts due to nonlinear processing, and a much reduced computational complexity.

/pdf/high-quality-linear-interpolation-for-demosaicing-of-bayer-3gdwthbsxt.pdf

High-quality linear interpolation for demosaicing of Bayer-patterned color images

Single sensor digital color cameras capture only one of the three primary colors at each pixel and a process called color demosaicking (CDM) is used to reconstruct the full color images. Most CDM algorithms assume the existence of high local spectral redundancy in estimating the missing color samples. However, for images with sharp color transitions and high color saturation, such an assumption may be invalid and visually unpleasant CDM errors will occur. In this paper, we exploit the image nonlocal redundancy to improve the local color reproduction result. First, multiple local direc- tional estimates of a missing color sample are computed and fused according to local gradients. Then, nonlocal pixels similar to the esti- mated pixel are searched to enhance the local estimate. An adaptive thresholding method rather than the commonly used nonlocal means filtering is proposed to improve the local estimate. This allows the final reconstruction to be performed at the structural level as op- posed to the pixel level. Experimental results demonstrate that the proposed local directional interpolation and nonlocal adaptive thresh- olding method outperforms many state-of-the-art CDM methods in reconstructing the edges and reducing color interpolation artifacts, leading to higher visual quality of reproduced color images. © 2011

/pdf/color-demosaicking-by-local-directional-interpolation-and-l59gsm41fl.pdf

Color demosaicking by local directional interpolation and nonlocal adaptive thresholding

Digital cameras sample scenes using a color filter array of mosaic pattern (e.g., the Bayer pattern). The demosaicking of the color samples is critical to the image quality. This paper presents a new color demosaicking technique of optimal directional filtering of the green-red and green-blue difference signals. Under the assumption that the primary difference signals (PDS) between the green and red/blue channels are low pass, the missing green samples are adaptively estimated in both horizontal and vertical directions by the linear minimum mean square-error estimation (LMMSE) technique. These directional estimates are then optimally fused to further improve the green estimates. Finally, guided by the demosaicked full-resolution green channel, the other two color channels are reconstructed from the LMMSE filtered and fused PDS. The experimental results show that the presented color demosaicking technique outperforms the existing methods both in PSNR measure and visual perception.

/pdf/color-demosaicking-via-directional-linear-minimum-mean-2l6zvxx4vm.pdf

Color demosaicking via directional linear minimum mean square-error estimation

Electronic cameras using a single CCD detector acquire scene color by subsampling in three, color planes and subsequently interpolating the information to reconstruct three, full-resolution color planes. The nature and size of the interpolation errors are a function of the algorithm used. When interpolation errors are propagated through the rest of the imaging chain, it becomes evident that synergistic effects among image processing operations must be considered when selecting and tuning an interpolation algorithm. This presentation demonstrates and comments on these image processing interactions.

Interactions between color plane interpolation and other image processing functions in electronic photography

A method of providing a refocused image, includes using a processor to perform the steps of acquiring at least two captured images of a scene with differing focus settings, using the at least two captured images to provide an all-in-focus image, generating a range map from the at least two captured images, locating a region-of-interest (ROI) window from the range map, and providing a refocused image from the all-in-focus image and the region-of-interest (ROI) window.

Refocusing images using scene captured images

PROBLEM TO BE SOLVED: To provide a simplified means for detecting and identifying a scene illuminant to use it in calculation of white balance correction and to correct the white balance for a color temperature change in each scene illuminant type. SOLUTION: The white balance correction method for a digital imaging device includes steps of determining the scene illuminant type from a scene brightness and one or more color coordinate values; determining the white balance parameter value for the scene illuminant type, the scene brightness, and one or more color coordinate values; providing at least one white balance correction curve; and determining the white balance correction from the white balance parameter value and at least one white balance correction curve for the determined scene illuminant type. COPYRIGHT: (C)2004,JPO

Method of automatically correcting white balance of digital image

PROBLEM TO BE SOLVED: To provide a method for reducing a collar aliasing artifacts from a collar digital image with a collar pixel. SOLUTION: The method of this invention has a stage for supplying a brightness signal and a chrominance signal from the collar digital image, a stage for using the brightness signal and the chrominance signal separating the image to textured regions and non-textured regions with a boundary, a stage for down sampling the chrominance signal and the textured regions mapping and a stage for forming the chrominance signal cleaned in the textured regions according to the boundary and the chrominance signal of the textured regions. It has a stage for forming the chrominance signal cleaned in the non-textured regions according to the chrominance signal, a stage for up sampling the chrominance signal which has noise cleaned, and a stage for using the brightness signal in order to form the digital collar image which reduced the collar aliasing artifacts and the chrominance signal which has cleaned noise up sampled. COPYRIGHT: (C)2004,JPO

Method for reducing collar aliasing artifacts from collar digital image

Adaptive Farbebeneninterpolation in einer elektronischen Einfachsensorfarbkamera

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.

Jr. James E. Adams

Author Tools

Chat about Author