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Standard test image

About: Standard test image is a research topic. Over the lifetime, 5217 publications have been published within this topic receiving 98486 citations.


Papers
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Book ChapterDOI
09 Jan 2020
TL;DR: The proposed model-agnostic approach consistently improves the performance of conventional SR networks on various benchmark SR datasets and effectively handles unknown SR kernels and can be applied to any existing model.
Abstract: Conventional supervised super-resolution (SR) approaches are trained with massive external SR datasets but fail to exploit desirable properties of the given test image. On the other hand, self-supervised SR approaches utilize the internal information within a test image but suffer from computational complexity in run-time. In this work, we observe the opportunity for further improvement of the performance of single-image super-resolution (SISR) without changing the architecture of conventional SR networks by practically exploiting additional information given from the input image. In the training stage, we train the network via meta-learning; thus, the network can quickly adapt to any input image at test time. Then, in the test stage, parameters of this meta-learned network are rapidly fine-tuned with only a few iterations by only using the given low-resolution image. The adaptation at the test time takes full advantage of patch-recurrence property observed in natural images. Our method effectively handles unknown SR kernels and can be applied to any existing model. We demonstrate that the proposed model-agnostic approach consistently improves the performance of conventional SR networks on various benchmark SR datasets.

33 citations

Patent
28 Sep 1994
TL;DR: In this article, a score recognition apparatus has a monitor receptive of the original image data and the synthetic image data to display an original image and a synthetic image in parallel to each other.
Abstract: A score recognition apparatus initially reads a given musical score to form an original image data. The original image data is analyzed to successively recognize symbols contained in the musical score to produce a score code data. Then, a synthetic image data of the musical score is reproduced according to the score code data. The score recognition apparatus has a monitor receptive of the original image data and the synthetic image data to display an original image and a synthetic image in parallel to each other. An operation command is inputted to command a screen operation of the displayed image. The pair of the original and synthetic image data are concurrently processed according to the operation command to execute the screen operation of both the original and synthetic images in parallel manner. Further, the pair of the original and synthetic image data are compared with each other to detect a discrepancy portion between the original image and the synthetic image to produce a discrepancy image data. The monitor is controlled according to the discrepancy image data to visually indicate the discrepancy portion.

33 citations

Book ChapterDOI
21 Oct 2005
TL;DR: A recent generic method for image classification based on ensemble of decision trees and random subwindows is applied and slightly adapt to achieve classification results close to the state of the art on a publicly available database of 10000 x-ray images.
Abstract: In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that goal, we apply and slightly adapt a recent generic method for image classification based on ensemble of decision trees and random subwindows. We obtain classification results close to the state of the art on a publicly available database of 10000 x-ray images. We also provide some clues to interpret the classification of each image in terms of subwindow relevance.

33 citations

Proceedings ArticleDOI
12 Dec 2008
TL;DR: Experimental results demonstrate that this new splicing detection algorithm is effective and reliable; indicating that the proposed approach has a broad application prospect.
Abstract: To implement image splicing detection a blind, passive and effective splicing detection scheme was proposed in this paper. The model was based on moment features extracted from the multi-size block discrete cosine transform (MBDCT) and some image quality metrics (IQMs) extracted from the given test image, which are sensitive to spliced image. This model can measure statistical differences between original image and spliced image. Experimental results demonstrate that this new splicing detection algorithm is effective and reliable; indicating that the proposed approach has a broad application prospect.

33 citations

Patent
25 Nov 1985
TL;DR: In this article, an image signal is segmented into regions of constant contrast or variance by applying a generalized likelihood ratio test to an image difference signal, which is then used to segment the image signal.
Abstract: An image signal is segmented into regions of constant contrast or variance by applying a generalized-likelihood-ratio test to an image difference signal.

33 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20231
20228
2021130
2020232
2019321
2018293