Feature Extraction Methods for Color Image Similarity

Chary, R. Venkata Ramana; Lakshmi, D. Rajya; Sunitha, K. V. N.

doi:10.48550/arxiv.1204.2336

Cited by 2 publications

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“…A completely automatic methodology creates a range of relevant colorization schemes using deep learning techniques and methods. We will train the system to identify basic colors [7][8][9][10][11][12] so that it can convert grey-scale color pictures to realistic colorizations. The test runs results in the Image Processing Research Lab (IPRL) show that the developed method is a better scheme to translate a gray-scale image into a color image.…”

Section: Introductionmentioning

confidence: 99%

An efficient Convolutional Neural Network (CNN) Based Image Colorization Technique

Wasim,

Ahmed,

Najib

et al. 2022

KJCIS

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Color in any image playing a vital role for better understanding and real image visualization. In past when there was no concept of colored images each black and white image need to highlight its own merits to explain an image in a better way, but when the concept of colored images started the things were totally changed to describe and highlight key features and attributes of an image in a more better and effective manner. The key issue is that the old images and videos are color less and in some cases it needs to be colorized. There are a number of applications are uses to colorized a gray-scale image. The authors of this paper also presented an efficient deep learning based image colorization technique. The proposed system maps all the gray contrast pixels of the image into its corresponding colored pixels to produce a colored image using Convolutional Neural Network (CNN) technique. The proposed system is totally an automated system and it avoid any manual work or user hand-define rules. The system first read the gray contrast of each pixel of image; matches function then create the corresponding color contrast from the color map and finally convert the gray contrast image into color image. The system tested around 100 images and found accuracy about 95%.

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Section: Introductionmentioning

confidence: 99%

An efficient Convolutional Neural Network (CNN) Based Image Colorization Technique

Wasim,

Ahmed,

Najib

et al. 2022

KJCIS

View full text Add to dashboard Cite

show abstract

“…The output result of the CNN processing architecture instruction that stepped forward the excellent resolution feats used with ground truth value is over-smoothed without high-frequency information [10]. The Superresolution Generative Adversarial Network (SRGAN), which uses the previously mentioned perceptual loss technique, improves the visible exceptionality of superb decision results [11].…”

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confidence: 99%

Evaluation of Image Support Resolution Deep Learning Technique based on PSNR Value

Azam,

Nouman

2022

KJCIS

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Machine learning is a subset of deep learning. Deep learning techniques have become increasingly popular in recent years for a variety of applications. Many techniques are used in image processing for image enhancement, object detection, and a variety of other purposes. Many deep learning techniques are important in single-image super-resolution (SISR). Many models are used for SISR, and they produce good perceptual and adversarial results. The procedure involves creating a high-resolution (HR) image from a low-resolution (LR) image, including some popular SISR methods: "Super-Resolution with Convolutional Neural Network" (SRCNN), "Super-Resolution Residual Network" (ResNet), "Super-Resolution Generative Adversarial Network" (SRGAN), and "Enhanced Super-Resolution Generative Adversarial Network" (ESRGAN). These deep learning models are layered, with each layer performing a different task for an LR image. These methods convert a single LR image into an HR image. A single LR image yields many high-resolution images. This paper creates a dataset of DSLR images for use with the SISR method. Deep learning technology is used to convert an LR image to an HR image. The HR images are then compared to the DSLR images. The "peak signal-to-noise ratio" (PSNR) and the "Structural Similarity Index" (SSI) are used to compare them (SSIM). When compared to the old method, the proposed method produces identical results. SSIM and PSNR are shown for both old and proposed approaches. Both approaches possess almost comparable results. The higher the PSNR value, the better the image quality. The same is the case with the SSIM value

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Feature Extraction Methods for Color Image Similarity

Cited by 2 publications

References 0 publications

An efficient Convolutional Neural Network (CNN) Based Image Colorization Technique

An efficient Convolutional Neural Network (CNN) Based Image Colorization Technique

Evaluation of Image Support Resolution Deep Learning Technique based on PSNR Value

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