Evaluations of Image Degradation from Multiple Scan-Print

2015 ◽  
Vol 7 (4) ◽  
pp. 55-65 ◽  
Author(s):  
Abhimanyu Singh Garhwal ◽  
Wei Qi Yan
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Visual Information ◽  
Fundamental Problem ◽  
Test Image ◽  
Image Degradation ◽  
Image Metrics ◽  
Objective Metrics ◽  
Multiple Scan ◽  
Degraded Images

Image degradation is a fundamental problem in digital image processing since it has been occurred in scan-print very often. The multiple rounds of scan-print usually lead to difficulties of perceiving visual information of an image. Therefore it is referred to as image degradation. In order to investigate the effects of the degradation, subjective and objective image metrics have been collected and are employed for measuring image quality. In this paper, those objective metrics will be harnessed for qualitatively analyzing the degraded images from multiple scan-print. The authors' contributions lie in evaluating the degradation by comparing each test image and its degraded ones with various objective metrics. Furthermore, the comparisons of the robustness and consistent of the metrics in support of their evaluations are analyzed.

scholarly journals Rectangular-Normalized Superpixel Entropy Index for Image Quality Assessment

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 947 ◽  
Author(s):  
Tao Lu ◽  
Jiaming Wang ◽  
Huabing Zhou ◽  
Junjun Jiang ◽  
Jiayi Ma ◽  
...  
Keyword(s):  
Image Quality ◽  
Quality Assessment ◽  
Visual Information ◽  
Image Quality Assessment ◽  
Spatial Configuration ◽  
Fundamental Problem ◽  
Weighted Average ◽  
Sliding Windows ◽  
Structure Information ◽  
Information Fidelity

Image quality assessment (IQA) is a fundamental problem in image processing that aims to measure the objective quality of a distorted image. Traditional full-reference (FR) IQA methods use fixed-size sliding windows to obtain structure information but ignore the variable spatial configuration information. In order to better measure the multi-scale objects, we propose a novel IQA method, named RSEI, based on the perspective of the variable receptive field and information entropy. First, we find that consistence relationship exists between the information fidelity and human visual of individuals. Thus, we reproduce the human visual system (HVS) to semantically divide the image into multiple patches via rectangular-normalized superpixel segmentation. Then the weights of each image patches are adaptively calculated via their information volume. We verify the effectiveness of RSEI by applying it to data from the TID2008 database and denoise algorithms. Experiments show that RSEI outperforms some state-of-the-art IQA algorithms, including visual information fidelity (VIF) and weighted average deep image quality measure (WaDIQaM).

APLIKASI PENAJAMAN CITRAA STREET VIEW MENGGUNAKAN METODE GRAMD SCHMIDT SPECTRAL SHARPENING DAN HIGH PASS FILTERING

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Erna Verawati ◽  
Surya Darma Nasution ◽  
Imam Saputra
Keyword(s):  
Image Processing ◽  
Visual Information ◽  
Intensity Difference ◽  
Original Image ◽  
Filtering Method ◽  
The Road ◽  
Street View ◽  
Result Show ◽  
Filter Process ◽  
High Pass

Sharpening the image of the road display requies a degree of brightness in the process of sharpening the image from the original image result of the improved image. One of the sharpening of the street view image is image processing. Image processing is one of the multimedia components that plays an important role as a form of visual information. There are many image processing methods that are used in sharpening the image of street views, one of them is the gram schmidt spectral sharpening method and high pass filtering. Gram schmidt spectral sharpening method is method that has another name for intensity modulation based on a refinement fillter. While the high pass filtering method is a filter process that btakes image with high intensity gradients and low intensity difference that will be reduced or discarded. Researce result show that the gram schmidt spectral sharpening method and high pass filtering can be implemented properly so that the sharpening of the street view image can be guaranteed sharpening by making changes frome the original image to the image using the gram schmidt spectral sharpening method and high pass filtering.Keywords: Image processing, gram schmidt spectral sharpening and high pass filtering.

scholarly journals Image Preprocessing in Classification and Identification of Diabetic Eye Diseases

2021 ◽  
Author(s):  
Rubina Sarki ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Yanchun Zhang ◽  
Jiangang Ma ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Geometric Transformation ◽  
Diabetic Patients ◽  
Fundus Image ◽  
Classification Problems ◽  
Specificity And Sensitivity ◽  
Retinal Fundus Image ◽  
Retinal Fundus ◽  
Traditional Image

AbstractDiabetic eye disease (DED) is a cluster of eye problem that affects diabetic patients. Identifying DED is a crucial activity in retinal fundus images because early diagnosis and treatment can eventually minimize the risk of visual impairment. The retinal fundus image plays a significant role in early DED classification and identification. An accurate diagnostic model’s development using a retinal fundus image depends highly on image quality and quantity. This paper presents a methodical study on the significance of image processing for DED classification. The proposed automated classification framework for DED was achieved in several steps: image quality enhancement, image segmentation (region of interest), image augmentation (geometric transformation), and classification. The optimal results were obtained using traditional image processing methods with a new build convolution neural network (CNN) architecture. The new built CNN combined with the traditional image processing approach presented the best performance with accuracy for DED classification problems. The results of the experiments conducted showed adequate accuracy, specificity, and sensitivity.

Image quality and dose differences caused by vendor-specific image processing of neonatal radiographs

2016 ◽  
Vol 46 (11) ◽  
pp. 1606-1613 ◽  
Author(s):  
William F. Sensakovic ◽  
M. Cody O’Dell ◽  
Haley Letter ◽  
Nathan Kohler ◽  
Baiywo Rop ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality

scholarly journals Improving Image Quality and Reducing Drift Problems via Automated Data Acquisition and Averaging in a Cs-Corrected TEM

2008 ◽  
Vol 16 (6) ◽  
pp. 36-39 ◽  
Author(s):  
E. Voelkl ◽  
B. Jiang ◽  
Z.R. Dai ◽  
J.P Bradley
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Data Acquisition ◽  
Information Content ◽  
Exposure Time ◽  
Image Acquisition ◽  
Ccd Camera ◽  
Automated Data Acquisition ◽  
Processing Steps

Image acquisition with a CCD camera is a single-press-button activity: after selecting exposure time and adjusting illumination, a button is pressed and the acquired image is perceived as the final, unmodified proof of what was seen in the microscope. Thus it is generally assumed that the image processing steps of e.g., “darkcurrent correction” and “gain normalization” do not alter the information content of the image, but rather eliminate unwanted artifacts.

Retinal Digital Image Quality Improvement as A Diabetes Retinopatic Disease Detection Effort

2020 ◽  
Vol 4 (2) ◽  
pp. 53-60
Author(s):  
Latifah Listyalina ◽  
Yudianingsih Yudianingsih ◽  
Dhimas Arief Dharmawan
Keyword(s):  
Image Processing ◽  
Diabetic Retinopathy ◽  
Image Quality ◽  
Digital Image ◽  
Signal To Noise Ratio ◽  
Histogram Equalization ◽  
Vital Role ◽  
Retinal Images ◽  
Signal To Noise ◽  
Noise Ratio

Image processing is a technical term useful for modifying images in various ways. In medicine, image processing has a vital role. One example of images in the medical world, namely retinal images, can be obtained from a fundus camera. The retina image is useful in the detection of diabetic retinopathy. In general, direct observation of diabetic retinopathy is conducted by a doctor on the retinal image. The weakness of this method is the slow handling of the disease. For this reason, a computer system is required to help doctors detect diabetes retinopathy quickly and accurately. This system involves a series of digital image processing techniques that can process retinal images into good quality images. In this research, a method to improve the quality of retinal images was designed by comparing the methods for adjusting histogram equalization, contrast stretching, and increasing brightness. The performance of the three methods was evaluated using Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Signal to Noise Ratio (SNR). Low MSE values and high PSNR and SNR values indicated that the image had good quality. The results of the study revealed that the image was the best to use, as evidenced by the lowest MSE values and the highest SNR and PSNR values compared to other techniques. It indicated that adaptive histogram equalization techniques could improve image quality while maintaining its information.

Can a channelized Hotelling observer assess image quality in acquired mammographic images of an anthropomorphic breast phantom including image processing?

2019 ◽  
Vol 46 (2) ◽  
pp. 714-725 ◽  
Author(s):  
C. Balta ◽  
R. W. Bouwman ◽  
I. Sechopoulos ◽  
M. J. M. Broeders ◽  
N. Karssemeijer ◽  
...  
Keyword(s):  
Image Processing ◽  
Image Quality ◽  
Mammographic Images ◽  
Hotelling Observer ◽  
Breast Phantom ◽  
Channelized Hotelling Observer

scholarly journals Image Processing Architectures

2018 ◽  
pp. 68-74
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Image Quality ◽  
Comparative Study ◽  
Focal Plane ◽  
Detailed Review ◽  
Communication Problem ◽  
Fast Processing ◽  
Processing Architectures ◽  
The Comparative Study

One type of signal processing is Image processing in which the input used as an image and the output might also be an image or a set of features that are related to the image. Images are handled as a 2D signal using image processing methods. For the fast processing of images, several architectures are suitable for different responsibilities in the image processing practices are important. Various architectures have been used to resolve the high communication problem in image processing systems. In this paper, we will yield a detailed review about these image processing architectures that are commonly used for the purpose of getting higher image quality. Architectures discussed are FPGA, Focal plane SIMPil, SURE engine. At the end, we will also present the comparative study of MSIMD architecture that will facilitate to understand best one.

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