Image enhancement, image quality, and noise

2005 ◽  
Author(s):  
Zia-ur Rahman ◽  
Daniel J. Jobson ◽  
Glenn A. Woodell ◽  
Glenn D. Hines
Keyword(s):  
Image Quality ◽  
Image Enhancement

Colour Image Enhancement using Perceptual Saturation and Vividness

2019 ◽  
Vol 2019 (1) ◽  
pp. 243-246
Author(s):  
Muhammad Safdar ◽  
Noémie Pozzera ◽  
Jon Yngve Hardeberg
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Test Performance ◽  
Appearance Model ◽  
Colour Space ◽  
Categorical Judgment ◽  
Saturation Scale ◽  
Colour Image ◽  
Colour Appearance ◽  
Future Test

A perceptual study was conducted to enhance colour image quality in terms of naturalness and preference using perceptual scales of saturation and vividness. Saturation scale has been extensively used for this purpose while vividness has been little used. We used perceptual scales of a recently developed colour appearance model based on Jzazbz uniform colour space. A two-fold aim of the study was (i) to test performance of recently developed perceptual scales of saturation and vividness compared with previously used hypothetical models and (ii) to compare performance and chose one of saturation and vividness scales for colour image enhancement in future. Test images were first transformed to Jzazbz colour space and their saturation and vividness were then decreased or increased to obtain 6 different variants of the image. Categorical judgment method was used to judge preference and naturalness of different variants of the test images and results are reported.

scholarly journals An Underwater Image Enhancement Algorithm Based on Generative Adversarial Network and Natural Image Quality Evaluation Index

2021 ◽  
Vol 9 (7) ◽  
pp. 691
Author(s):  
Kai Hu ◽  
Yanwen Zhang ◽  
Chenghang Weng ◽  
Pengsheng Wang ◽  
Zhiliang Deng ◽  
...  
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Quality Evaluation ◽  
High Efficiency ◽  
Natural Image ◽  
High Quality ◽  
Generative Adversarial Network ◽  
Image Quality Evaluation ◽  
Adversarial Network ◽  
Underwater Image

When underwater vehicles work, underwater images are often absorbed by light and scattered and diffused by floating objects, which leads to the degradation of underwater images. The generative adversarial network (GAN) is widely used in underwater image enhancement tasks because it can complete image-style conversions with high efficiency and high quality. Although the GAN converts low-quality underwater images into high-quality underwater images (truth images), the dataset of truth images also affects high-quality underwater images. However, an underwater truth image lacks underwater image enhancement, which leads to a poor effect of the generated image. Thus, this paper proposes to add the natural image quality evaluation (NIQE) index to the GAN to provide generated images with higher contrast and make them more in line with the perception of the human eye, and at the same time, grant generated images a better effect than the truth images set by the existing dataset. In this paper, several groups of experiments are compared, and through the subjective evaluation and objective evaluation indicators, it is verified that the enhanced image of this algorithm is better than the truth image set by the existing dataset.

Digital Mammograms with Image Enhancement Techniques for Breast Cancer Detection: A Systematic Review

2021 ◽  
Vol 17 ◽  
Author(s):  
Saifullah Harith Suradi ◽  
Kamarul Amin Abdullah
Keyword(s):  
Breast Cancer ◽  
Image Quality ◽  
Image Enhancement ◽  
Cancer Detection ◽  
Mean Squared Error ◽  
Survival Rates ◽  
Curvelet Transform ◽  
Breast Cancer Detection ◽  
Online Databases ◽  
Fast Discrete Curvelet Transform

Background: Digital mammograms with appropriate image enhancement techniques will improve breast cancer detection, and thus increase the survival rates. The objectives of this study were to systematically review and compare various image enhancement techniques in digital mammograms for breast cancer detection. Methods: A literature search was conducted with the use of three online databases namely, Web of Science, Scopus, and ScienceDirect. Developed keywords strategy was used to include only the relevant articles. A Population Intervention Comparison Outcomes (PICO) strategy was used to develop the inclusion and exclusion criteria. Image quality was analyzed quantitatively based on peak signal-noise-ratio (PSNR), Mean Squared Error (MSE), Absolute Mean Brightness Error (AMBE), Entropy, and Contrast Improvement Index (CII) values. Results: Nine studies with four types of image enhancement techniques were included in this study. Two studies used histogram-based, three studies used frequency-based, one study used fuzzy-based and three studies used filter-based. All studies reported PSNR values whilst only four studies reported MSE, AMBE, Entropy and CII values. Filter-based was the highest PSNR values of 78.93, among other types. For MSE, AMBE, Entropy, and CII values, the highest were frequency-based (7.79), fuzzy-based (93.76), filter-based (7.92), and frequency-based (6.54) respectively. Conclusion: In summary, image quality for each image enhancement technique is varied, especially for breast cancer detection. In this study, the frequency-based of Fast Discrete Curvelet Transform (FDCT) via the UnequiSpaced Fast Fourier Transform (USFFT) shows the most superior among other image enhancement techniques.

scholarly journals Application of high frequency filtration to remove cloudiness in Earth remote sensing images

2020 ◽  
Vol 223 ◽  
pp. 02010
Author(s):  
Valeriy Tutatchikov ◽  
Mikhail Noskov
Keyword(s):  
Remote Sensing ◽  
Image Quality ◽  
Image Enhancement ◽  
High Frequency ◽  
Digital Processing ◽  
Remote Sensing Images ◽  
Earth Remote Sensing ◽  
High Clouds

At present, methods of digital processing of Earth remote sensing images are widely used to improve the image quality. For example, many images are discarded due to high clouds in the images, which obscure objects of interest. In this paper, the possibility of using high- frequency global filters to reduce cloudiness in the image is considered, and the results of image enhancement are shown.

scholarly journals UCAN: A Learning-based Model to Enhance Poorly Exposed Images

2020 ◽  
Author(s):  
Lucas R. V. Messias ◽  
Cristiano R. Steffens ◽  
Paulo L. J. Drews-Jr ◽  
Silvia S. C. Botelho
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Dynamic Range ◽  
Low Contrast ◽  
Qualitative And Quantitative ◽  
Fast Learning ◽  
Proposed Model ◽  
Quantitative Results ◽  
Critical Process ◽  
Poor Lighting

Image enhancement is a critical process in imagebased systems. In these systems, image quality is a crucial factor to achieve a good performance. Scenes with a dynamic range above the capability of the camera or poor lighting are challenging conditions, which usually result in low contrast images, and, with that, we can have the underexposure and/or overexposure problem. In this work, our aim is to restore illexposed images. For this purpose, we present UCAN, a small and fast learning-based model capable to restore and enhance poorly exposed images. The obtained results are evaluated using image quality indicators which show that the proposed network is able to improve images damaged by real and simulated exposure. Qualitative and quantitative results show that the proposed model outperforms the existing models for this objective.

Perceptual Quality Assessment of Low-light Image Enhancement

2021 ◽  
Vol 17 (4) ◽  
pp. 1-24
Author(s):  
Guangtao Zhai ◽  
Wei Sun ◽  
Xiongkuo Min ◽  
Jiantao Zhou
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Dynamic Range ◽  
State Of The Art ◽  
Perceptual Quality ◽  
Low Light ◽  
Light Image ◽  
Light Enhancement

Low-light image enhancement algorithms (LIEA) can light up images captured in dark or back-lighting conditions. However, LIEA may introduce various distortions such as structure damage, color shift, and noise into the enhanced images. Despite various LIEAs proposed in the literature, few efforts have been made to study the quality evaluation of low-light enhancement. In this article, we make one of the first attempts to investigate the quality assessment problem of low-light image enhancement. To facilitate the study of objective image quality assessment (IQA), we first build a large-scale low-light image enhancement quality (LIEQ) database. The LIEQ database includes 1,000 light-enhanced images, which are generated from 100 low-light images using 10 LIEAs. Rather than evaluating the quality of light-enhanced images directly, which is more difficult, we propose to use the multi-exposure fused (MEF) image and stack-based high dynamic range (HDR) image as a reference and evaluate the quality of low-light enhancement following a full-reference (FR) quality assessment routine. We observe that distortions introduced in low-light enhancement are significantly different from distortions considered in traditional image IQA databases that are well-studied, and the current state-of-the-art FR IQA models are also not suitable for evaluating their quality. Therefore, we propose a new FR low-light image enhancement quality assessment (LIEQA) index by evaluating the image quality from four aspects: luminance enhancement, color rendition, noise evaluation, and structure preserving, which have captured the most key aspects of low-light enhancement. Experimental results on the LIEQ database show that the proposed LIEQA index outperforms the state-of-the-art FR IQA models. LIEQA can act as an evaluator for various low-light enhancement algorithms and systems. To the best of our knowledge, this article is the first of its kind comprehensive low-light image enhancement quality assessment study.

scholarly journals Choosing the Optimal Spatial Domain Measure of Enhancement for Mammogram Images

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Karen Panetta ◽  
Arash Samani ◽  
Sos Agaian
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Visual Image ◽  
Imaging Systems ◽  
Medical Professionals ◽  
Optimal Operating ◽  
Negative Effect ◽  
Detection And Diagnosis ◽  
Mammogram Images

Medical imaging systems often require image enhancement, such as improving the image contrast, to provide medical professionals with the best visual image quality. This helps in anomaly detection and diagnosis. Most enhancement algorithms are iterative processes that require many parameters be selected. Poor or nonoptimal parameter selection can have a negative effect on the enhancement process. In this paper, a quantitative metric for measuring the image quality is used to select the optimal operating parameters for the enhancement algorithms. A variety of measures evaluating the quality of an image enhancement will be presented along with each measure’s basis for analysis, namely, on image content and image attributes. We also provide guidelines for systematically choosing the proper measure of image quality for medical images.

scholarly journals Tinjauan Literatur Sistematik tentang Structural Similarity Index Measure untuk Deteksi Anomali Gambar

2021 ◽  
Vol 7 (2) ◽  
pp. 75
Author(s):  
Halim Bayuaji Sumarna ◽  
Ema Utami ◽  
Anggit Dwi Hartanto
Keyword(s):  
Image Quality ◽  
Image Enhancement ◽  
Human Visual System ◽  
Vision System ◽  
Similarity Index ◽  
Structural Similarity ◽  
Human Vision ◽  
Human Vision System ◽  
Structural Similarity Index ◽  
Index Measure

Image enhancement merupakan prosedur yang digunakan untuk memproses gambar sehingga dapat memperbaiki atau meningkatkan kualitas gambar agar selanjutnya dapat dianalis untuk tujuan tertentu. Ada banyak algoritma image enhancement yang dapat diterapkan pada suatu gambar, salah satunya dapat menggunakan algoritma structural similarity index measure (SSIM), algoritma ini berfungsi sebagai alat ukur dalam menilai kualitas gambar, bekerja dengan membandingkan fitur structural dari gambar, dan kualitas gambar dijelaskan oleh kesamaan structural. Selain untuk menilai kualitas suatu gambar, SSIM dapat menjadi metode dalam menganalisis perbedaan gambar, sehingga diketahui anomali dari perbandingan dua gambar berdasarkan data structural dari sebuah gambar. Tinjauan literature sistematis ini digunakan untuk menganalisis dan fokus pada algoritma SSIM dalam mengetahui anomaly 2 gambar yang terlihat mirip secara human visual system. Hasil sistematis review menunjukkan bahwa penggunaan algoritma SSIM dalam menilai kualitas gambar berkorelasi kuat dengan HVS (Human Vision System) dan dalam deteksi anomaly gambar menghasilkan akurasi yang berbeda, karena terpengaruh intensitas cahaya dan posisi kamera dalam mengambil gambar sebagai dataset.Kata Kunci— SSIM, anomaly, gambar, deteksiImage enhancement is a procedure used to process images so that they can correct or improve image quality so that they can then be analyzed for specific purposes. Many image enhancement algorithms can be applied to an image. one of the usable methods is the structural similarity index measure (SSIM) algorithm, this algorithm serves as a measuring tool in assessing image quality. It works by comparing the structural features of images, and the image quality is explained by structural similarity. In addition to assessing the quality of an image, SSIM can be a method of analyzing image differences. So, the anomalies are known from the comparison of two images based on the structural data from an image. This systematic literature review is used to analyze and focus on the SSIM algorithm in knowing anomaly 2 images that look similar to the human visual system. Systematic review results show that the use of the SSIM algorithm in assessing image quality is strongly correlated with HVS (Human Vision System). In anomaly detection of images produces different accuracy because it is affected by light intensity and camera position in taking pictures as a dataset.Keywords— SSIM, anomaly, gambar, deteksi

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