scispace - formally typeset
Topic

Grayscale

About: Grayscale is a(n) research topic. Over the lifetime, 13278 publication(s) have been published within this topic receiving 156084 citation(s). The topic is also known as: grayscale image & black-and-white image.
Papers
More filters

Journal ArticleDOI
TL;DR: It is proved analytically and shown experimentally that the peak signal-to-noise ratio of the marked image generated by this method versus the original image is guaranteed to be above 48 dB, which is much higher than that of all reversible data hiding techniques reported in the literature.
Abstract: A novel reversible data hiding algorithm, which can recover the original image without any distortion from the marked image after the hidden data have been extracted, is presented in this paper. This algorithm utilizes the zero or the minimum points of the histogram of an image and slightly modifies the pixel grayscale values to embed data into the image. It can embed more data than many of the existing reversible data hiding algorithms. It is proved analytically and shown experimentally that the peak signal-to-noise ratio (PSNR) of the marked image generated by this method versus the original image is guaranteed to be above 48 dB. This lower bound of PSNR is much higher than that of all reversible data hiding techniques reported in the literature. The computational complexity of our proposed technique is low and the execution time is short. The algorithm has been successfully applied to a wide range of images, including commonly used images, medical images, texture images, aerial images and all of the 1096 images in CorelDraw database. Experimental results and performance comparison with other reversible data hiding schemes are presented to demonstrate the validity of the proposed algorithm.

2,004 citations


Journal ArticleDOI
Luc Vincent1
TL;DR: An algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm.
Abstract: Two different formal definitions of gray-scale reconstruction are presented. The use of gray-scale reconstruction in various image processing applications discussed to illustrate the usefulness of this transformation for image filtering and segmentation tasks. The standard parallel and sequential approaches to reconstruction are reviewed. It is shown that their common drawback is their inefficiency on conventional computers. To improve this situation, an algorithm that is based on the notion of regional maxima and makes use of breadth-first image scannings implemented using a queue of pixels is introduced. Its combination with the sequential technique results in a hybrid gray-scale reconstruction algorithm which is an order of magnitude faster than any previously known algorithm. >

1,975 citations


16


Book ChapterDOI
08 Oct 2016-
TL;DR: This paper proposes a fully automatic approach to colorization that produces vibrant and realistic colorizations and shows that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder.
Abstract: Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches have either relied on significant user interaction or resulted in desaturated colorizations. We propose a fully automatic approach that produces vibrant and realistic colorizations. We embrace the underlying uncertainty of the problem by posing it as a classification task and use class-rebalancing at training time to increase the diversity of colors in the result. The system is implemented as a feed-forward pass in a CNN at test time and is trained on over a million color images. We evaluate our algorithm using a “colorization Turing test,” asking human participants to choose between a generated and ground truth color image. Our method successfully fools humans on 32 % of the trials, significantly higher than previous methods. Moreover, we show that colorization can be a powerful pretext task for self-supervised feature learning, acting as a cross-channel encoder. This approach results in state-of-the-art performance on several feature learning benchmarks.

1,949 citations


Journal ArticleDOI
TL;DR: This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.
Abstract: Sparse representations of signals have drawn considerable interest in recent years. The assumption that natural signals, such as images, admit a sparse decomposition over a redundant dictionary leads to efficient algorithms for handling such sources of data. In particular, the design of well adapted dictionaries for images has been a major challenge. The K-SVD has been recently proposed for this task and shown to perform very well for various grayscale image processing tasks. In this paper, we address the problem of learning dictionaries for color images and extend the K-SVD-based grayscale image denoising algorithm that appears in . This work puts forward ways for handling nonhomogeneous noise and missing information, paving the way to state-of-the-art results in applications such as color image denoising, demosaicing, and inpainting, as demonstrated in this paper.

1,694 citations


18


Journal ArticleDOI
TL;DR: Although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques, and a graphical measure called Hosaka plots can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.
Abstract: A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers' response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.

1,445 citations


Network Information
Related Topics (5)
Image processing

229.9K papers, 3.5M citations

93% related
Image segmentation

79.6K papers, 1.8M citations

92% related
Edge detection

25.5K papers, 486.4K citations

92% related
Segmentation

63.2K papers, 1.2M citations

92% related
Standard test image

5.2K papers, 98.4K citations

92% related
Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202211
2021530
2020785
20191,156
20181,192
2017993

Top Attributes

Show by:

Topic's top 5 most impactful authors

Chin-Chen Chang

16 papers, 227 citations

Sos S. Agaian

11 papers, 248 citations

Konstantinos N. Plataniotis

9 papers, 466 citations

Wilfried Philips

8 papers, 105 citations

Hitoshi Kiya

6 papers, 140 citations