Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 10,000 papers for “Image resolution”

Image resolution

John B. Ludlow, Andreacute Mol · Oral Surgery Oral Medicine Oral Pathology Oral Radiology and Endodontology · 2001

Cited by 0

Image Resolution

Stan Z. Li, Anil Jain · Encyclopedia of Biometrics · 2009

Cited by 0

Image quality and image resolution

Ralf Reulke, Andreas Eckardt · 2013

13 years after the introduction of the first digital airborne mapping camera in the ISPRS conference 2000 in Amsterdam, several digital cameras with more than 100 MPixels per image have been made available. They are now well established in the market and have replaced the analogue mapping cameras. A general improvement in image quality accompanied the digital camera development. The signal-to-noise ratio and the dynamic range are significantly better than with the analogue cameras. In addition, digital cameras can be spectrally and radiometrically calibrated. The use of these cameras required a rethinking in many places. In the recent years, some activities took place that will lead to a better understanding of the cameras and the data produced by these cameras. Several projects, like the projects of the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) or EuroSDR (European Spatial Data Research), were conducted to test and compare the performance of different cameras. This paper will give an overview of standards and alternative approaches in image quality criteria's, and the derivation and use of these parameters.

Cited by 5

Robust High Resolution Image from the Low Resolution Satellite Image

Ch. Venkateswara Rao, Dr. V. Srinivasa Rao, K. Srinivas et al. · 2014

Abstract — In this paper, we propose a framework detecting and locating the land cover classes from a low-resolution image, which can play a very important role in the satellite surveillance image from the MODIS data. The lands cover classes by constructing superresolution images from the MODIS data. The highest resolution of the MODIS images is 250 meters per pixel. By magnifying and de-blurring the low-resolution satellite image through the kernel regression. SR reconstruction is image interpolation that has been used to increase the size of a single image. The SRKR algorithm takes a single low-resolution image and generates a de-blurred high-resolution image. We perform bi-cubic interpolation on the input low-resolution image (LR) with a desired scaling factor. Finally, the KR model is then used to generate the de-blurred HR image. K-means is one of the simplest unsupervised learning algorithms that solve the well-known clustering problem, which generates a specific number of disjoint, flat (non-hierarchical) clusters. K-means clustering is employ in order to compare MODIS data and recognize land cover type, i.e., “Forest”, “Land”, “sea”, and “Ice”. Index Terms — Satellite LR Image, Super-Resolution Image, MODIS Data

Cited by 0

High-resolution image reconstruction from lower-resolution image sequences and space-varying image restoration

A. Murat Tekalp, M.K. Ozkan, M. Ibrahim Sezan · 1992

The authors address the problem of reconstruction of a high-resolution image from a number of lower-resolution (possibly noisy) frames of the same scene where the successive frames are uniformly based versions of each other at subpixel displacements. In particular, two previously proposed methods, a frequency-domain method and a method based on projections onto convex sets (POCSs), are extended to take into account the presence of both sensor blurring and observation noise. A new two-step procedure is proposed, and it is shown that the POCS formulation presented for the high-resolution image reconstruction problem can also be used as a new method for the restoration of spatially invariant blurred images. Some simulation results are provided.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Cited by 309

Resolution-Aware Network for Image Super-Resolution

Yifan Wang, Lijun Wang, Hongyu Wang et al. · IEEE Transactions on Circuits and Systems for Video Technology · 2018

In existing deep network-based image super-resolution (SR) methods, each network is only trained for a fixed upscaling factor and can hardly generalize to unseen factors at test time, which is non-scalable in real applications. To mitigate this issue, this paper proposes a resolution-aware network (RAN) for simultaneous SR of multiple factors. The key insight is that SR of multiple factors is essentially different but also shares common operations. To attain stronger generalization across factors, we design an upsampling network (U-Net) consisting of several sub-modules, in which each sub-module implements an intermediate step of the overall image SR and can be shared by SR of different factors. A decision network (D-Net) is further adopted to identify the quality of the input low-resolution image and adaptively select suitable sub-modules to perform SR. U-Net and D-Net together constitute the proposed RAN model, and are jointly trained using a new hierarchical loss function on SR tasks of multiple factors. Experimental evaluations demonstrate that the proposed RAN compares favorably against the state-of-the-art methods and its performance can well generalize across different upscaling factors.

Cited by 45

Subjective Assessment About Super Resolution Image Resolution

Seiichi Gohshi, Hiroyuki Sekiguchi, Yoshiyasu Shimizu et al. · Zenodo (CERN European Organization for Nuclear Research) · 2012

Super resolution (SR) technologies are now being applied to video to improve resolution. Some TV sets are now equipped with SR functions. However, it is not known if super resolution image reconstruction (SRR) for TV really works or not. Super resolution with non-linear signal processing (SRNL) has recently been proposed. SRR and SRNL are the only methods for processing video signals in real time. The results from subjective assessments of SSR and SRNL are described in this paper. SRR video was produced in simulations with quarter precision motion vectors and 100 iterations. These are ideal conditions for SRR. We found that the image quality of SRNL is better than that of SRR even though SRR was processed under ideal conditions.

Cited by 0Open access

Selective Image Super-Resolution

Ju Suna, Qiang Chenb, Shuicheng Yanb et al. · 2015

In this paper we propose a vision system that performs image Super Resolu-tion (SR) with selectivity. Conventional SR techniques, either by multi-image fusion or example-based construction, have failed to capitalize on the intrinsic structural and semantic context in the image, and performed “blind ” resolu-tion recovery to the entire image area. By comparison, we advocate example-based selective SR whereby selectivity is exemplified in three aspects: region selectivity (SR only at object regions), source selectivity (object SR with trained object dictionaries), and refinement selectivity (object boundaries refinement using matting). The proposed system takes over-segmented low-resolution images as inputs, assimilates recent learning techniques of sparse coding (SC) and grouped multi-task lasso (GMTL), and leads eventually to a framework for joint figure-ground separation and interest object SR. The efficiency of our framework is manifested in our experiments with subsets of the VOC2009 and MSRC datasets. We also demonstrate several interesting vision applications that can build on our system.

Cited by 0

USAF resolution target image.

Dongsuk Shin (363889), Mark C. Pierce (307100), Ann M. Gillenwater (363891) et al. · Figshare · 2015

Cited by 0Open access