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About 10,000 papers for “Image processing”

Image processing apparatus and image processing method

FUCHIGAMI, TAKAHIRO, TABATA, SUNAO · Institutional Repository of Xi'an Institute of Optics and Fine Mechanics, Chinese Academy of Sciences (Xian Institute of Optics and Precision Mechanics) · 2003

In an image processing apparatus, first image data and first discrimination data is generated on the basis of page information described in a page description language. Second discrimination data is generated on the basis of the generated first image data and first discrimination data. Second image data is generated on the basis of the generated second discrimination data and the first image data. An image processing is performed based on the generated second image data and second discrimination data, and the processed data is transferred on a printing medium.

Cited by 0

Digital Image Processing Image Processing and Restoration

Bryan J. Bollinger, Professor Hairong Qi · 2007

The purpose of this paper is to explore techniques for image enhancement and recovery, especially as they relate to reversing or minimizing the effects of noise degradation. The specific goal of the paper is to attempt to recover readable characters from a license plate image. I start with a short background on the importance of enhancement and then cover some of the technical approaches and techniques. The baseline techniques are some simple enhancement approaches (usharp masking, Sobel edge detection, gray level slicing) and then I move one to more specific denoising filters (contraharmonic, wiener. notch). The final topic is an IEEE transactions paper on nonlinear extrapolation in frequency space that is implemented and tested. I found most of these techniques to be severely inadequate for the level of damage in the target image. Background Image enhancement is the process of making an image more pleasing to a human viewer or more suitable for a specific task. Image enhancement helps us to more quickly and accurately understand the vast amounts of data acquired everyday in real life scenarios. For instance, MRI images can be quite useful in the screening of cancer, but the cancer itself can be difficult to visually separate from living tissue. We can use image enhancement techniques to more drastically highlight the differences and make it easier for a human being to detect the cancer from the images. Another use for image enhancement might be to restore an old film that has been damaged by dust and decay. The frames of the film can be scrubbed of imperfections using certain enhancement techniques so that the audience isn’t distracted by the noise and can better enjoy the film. In our scenario, we have a license plate that has been captured by a real world system. The circumstances of the generation of the image are unknown, but it could easily be that this particular vehicle was nearby or involved with the commission of a crime, and we would like be able to track down the owner of the vehicle via the license plate. Unfortunately, the image is badly degraded and needs to be repaired so that the characters of the license plate can be read.

Cited by 0

Image processing

O. I. Belousov, Z. M. Albekova · Научные тенденции: Вопросы точных и технических наук · 2018

Cited by 3Open access

Image Processing

Rama Chellappa, Azriel Rosenfeld · Elsevier eBooks · 2003

Cited by 3

Image Processing

Andrew P. King · 2021

Computerised image processing techniques are playing a growing role in manipulating these images in order to aid in the clinical decision-making process. This chapter focuses on two fields: image filtering and image segmentation. Image filtering techniques can be broken down into spatial domain operations and frequency domain operations. Spatial domain operations can be further broken down into point-processing and mask-processing operations. Both produce a new version of the original image; the difference lies in which pixel/voxel intensities are used to determine the new intensity at any given point. As well as performing image filtering operations in the spatial domain, similar operations can be performed in the frequency domain. Frequency domain representations of one-dimensional signals are based on the fact that any signal can be expressed as a weighted sum of sine waves of different frequencies and phases. In reality, many segmentation problems are challenging than this and a more complex series of operations may be required to produce satisfactory segmentations.

Cited by 2

Image Processing

Franz, Paris · Figshare · 2024

Unprocessed cVMI images and script for processing the images to prepare for reconstruction code.

Cited by 0Open access

Image processing.

Devin Thomas Krasowski (10090541), Bridget Wadzuk (9088963), Brian Jacko (10090544) · Figshare · 2021

<p>(A) Zoomed in colored image. (B) colored image after thresholding out all non "green" cells and cropping. (C) full thermal image. (D) thermal image cropped by ROI.</p>

Cited by 0Open access

Image Processing

NASA Technical Reports Server (NASA)

Electronic Imagery, Inc.'s ImageScale Plus software, developed through a Small Business Innovation Research (SBIR) contract with Kennedy Space Flight Center for use on space shuttle Orbiter in 1991, enables astronauts to conduct image processing, prepare electronic still camera images in orbit, display them and downlink images to ground based scientists for evaluation. Electronic Imagery, Inc.'s ImageCount, a spin-off product of ImageScale Plus, is used to count trees in Florida orange groves. Other applications include x-ray and MRI imagery, textile designs and special effects for movies. As of 1/28/98, company could not be located, therefore contact/product information is no longer valid.

Cited by 0Open access

Image processing

Lee, T-Y, Blake, M, Chen, X et al. · UCL Discovery (University College London) · 2007

Cited by 0

Image processing

Chan, Andy Bing-Bill · cIRcle (University of British Columbia) · 2009

In this thesis, we consider the restoration of multiple grey levels image. The problem
\nis to clean up or restore the dirty picture, that is, to construct an estimate of the true
\nimage from a noisy picture of that true image. Following a method proposed by Meloche
\nand Zamar (1994), we estimate the colour at each site by a function of the data available
\nin a neighbourhood of that site. In this approach, the local characteristics of that image,
\nthat is, the frequency with which each pattern appears in the true unobserved image are
\nparticularly important. We will propose a family of unbiased estimates of the pattern
\ndistribution and the noise level which are used in the restoration process. We will use
\nour estimates of the pattern distribution in an attempt to select the best neighbourhood
\nshape for the restoration process.

Cited by 0

Image processing

Thomas J. Naughton, John B. Mc Donald, Bahram Javidi · 2015

Abstract. We compress phase-shift digital holograms (whole Fresnel fields) for the transmission of three-dimensional images. For real-time networking applications, the time required to compress can be as critical as compression rate. We achieve lossy compression through quantization of both the real and imaginary streams, followed by a bit packing operation. Compression losses in the reconstructed objects were quantified. We define a speedup metric that combines space gains due to compression with temporal overheads due to compression routine and transmission serialization. We empirically verify transmission speedup due to compression, using a special-purpose

Cited by 0