Automatic Selection of Parameters for Document Image Enhancement Using Image Quality Assessment

2016 ◽  
Author(s):  
Ritu Garg ◽  
Santanu Chaudhury
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Image Quality Assessment ◽  
Document Image ◽  
Automatic Selection ◽  
Selection Of

Document Image Quality Assessment with Relaying Reference to Determine Minimum Readable Resolution for Compression

2020 ◽  
Vol 2020 (9) ◽  
pp. 323-1-323-8
Author(s):  
Litao Hu ◽  
Zhenhua Hu ◽  
Peter Bauer ◽  
Todd J. Harris ◽  
Jan P. Allebach
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Quality Assessment ◽  
Research Area ◽  
Input Image ◽  
Quality Score ◽  
Document Image ◽  
Digital Cameras ◽  
Active Research ◽  
Traditional Approaches

Image quality assessment has been a very active research area in the field of image processing, and there have been numerous methods proposed. However, most of the existing methods focus on digital images that only or mainly contain pictures or photos taken by digital cameras. Traditional approaches evaluate an input image as a whole and try to estimate a quality score for the image, in order to give viewers an idea of how “good” the image looks. In this paper, we mainly focus on the quality evaluation of contents of symbols like texts, bar-codes, QR-codes, lines, and hand-writings in target images. Estimating a quality score for this kind of information can be based on whether or not it is readable by a human, or recognizable by a decoder. Moreover, we mainly study the viewing quality of the scanned document of a printed image. For this purpose, we propose a novel image quality assessment algorithm that is able to determine the readability of a scanned document or regions in a scanned document. Experimental results on some testing images demonstrate the effectiveness of our method.

scholarly journals SEDIQA: Sound Emitting Document Image Quality Assessment in a Reading Aid for the Visually Impaired

2021 ◽  
Author(s):  
Jane Courtney
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Character Recognition ◽  
Optical Character Recognition ◽  
Visually Impaired ◽  
Image Quality Assessment ◽  
Document Image ◽  
Read Aloud ◽  
Reference Image ◽  
Reading Aids

For Visually impaired People (VIPs), the ability to convert text to sound can mean a new level of independence or the simple joy of a good book. With significant advances in Optical Character Recognition (OCR) in recent years, a number of reading aids are appearing on the market. These reading aids convert images captured by a camera to text which can then be read aloud. However, all of these reading aids suffer from a key issue – the user must be able to visually target the text and capture an image of sufficient quality for the OCR algorithm to function – no small task for VIPs. In this work, a Sound-Emitting Document Image Quality Assessment metric (SEDIQA) is proposed which allows the user to hear the quality of the text image and automatically captures the best image for OCR accuracy. This work also includes testing of OCR performance against image degradations, to identify the most significant contributors to accuracy reduction. The proposed No-Reference Image Quality Assessor (NR-IQA) is validated alongside established NR-IQAs and this work includes insights into the performance of these NR-IQAs on document images.

scholarly journals Image Enhancement and Feature Extraction of Cytomegalovirus Image using Hierarchical Ranking Convolutional Neural Network (HR-CNN)

2019 ◽  
Vol 8 (12) ◽  
pp. 3816-3820
Keyword(s):  
Neural Network ◽  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Mean Square Error ◽  
Image Quality Assessment ◽  
Similarity Index ◽  
Mean Square ◽  
Structure Similarity Index ◽  
Structure Similarity

Human Cytomegalovirus is becoming a common issue around the globe , mainly it deals with the infection of the fetus in the womb. Digital image processing plays a vital role in various fields especially in the field of medicine to have a better quality of image of viruses in various forms. To have better clarity of images even in microscopic images there might be some flaws in detection of viruses because of the intensities which occur due to atmospheric lights, to overcome the flaws in microscopic images there comes a technique image enhancement to overcome noise in images especially distortion free images to be produced based on some image quality assessment and to reduce noise in an image without any loss of information. In this paper the proposed methodology called Hierarchical Ranking Convolution Neural Network is introduced based on Upward/Downward hierarchy and Forward/Backward Hierarchy to extract features and to provide intensified image of the virus. Image quality assessment is done with the parameters and evaluated using Mean Square Error, Peak signal to Noise Ratio, Root Mean Square Error, Structure Similarity Index, Mean Structure Similarity Index to prove the accuracy.

A Novel Full-Reference Color Image Quality Assessment Based on Energy Computation in the Wavelet Domain

2013 ◽  
Vol 22 (2) ◽  
pp. 155-177
Author(s):  
M.C. Hanumantharaju ◽  
M. Ravishankar ◽  
D.R. Rameshbabu ◽  
V.N. Manjunath Aradhya
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Image Enhancement ◽  
Image Quality Assessment ◽  
Color Image ◽  
Energy Levels ◽  
Assessment Method ◽  
Wavelet Domain ◽  
Energy Estimation ◽  
Full Reference

AbstractThis article presents a novel full-reference (FR) image quality assessment (QA) algorithm by depicting the sub-band characteristics in the wavelet domain. The proposed image quality assessment method is based on energy estimation in the wavelet-transformed image. Image QA is achieved by applying a multilevel wavelet decomposition on both the original and the enhanced image. Next, the wavelet energy (WE) and vector are computed to obtain the percentage of the energy that corresponds to the approximation and the details, respectively. Further, the approximate and detailed energy levels of both the original and the enhanced images are compared to formulate an image quality assessment. Numerous experiments are conducted on a dozen of image enhancement algorithms. The results presented show that the image with poor contrast in the foreground than the background has continuous regular coefficient values. The probability density function for such an image has a relatively lower WE and skewness compared with the background. The proposed scheme not only evaluates the global information of an image but also estimates the fine, detailed changes in an enhanced image. Thus, the proposed metric serves as an objective and effective FR criterion for color image QA. The experimental results presented confirm that the proposed WE metric is an efficient and useful metric for evaluating the quality of the color image enhancement.

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