scholarly journals A No-Reference Sharpness Metric Based on Structured Ringing for JPEG2000 Images

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Zhipeng Cao ◽  
Zhenzhong Wei ◽  
Guangjun Zhang
Keyword(s):  
Edge Detection ◽  
Anisotropic Diffusion ◽  
Experimental Results ◽  
Reference Image ◽  
Image Sharpness ◽  
Ringing Artifacts ◽  
Sharpness Metric ◽  
Sharpness Metrics ◽  
Degraded Images

This work presents a no-reference image sharpness metric based on human blur perception for JPEG2000 compressed image. The metric mainly uses a ringing measure. And a blurring measure is used for compensation when the blur is so severe that ringing artifacts are concealed. We used the anisotropic diffusion for the preliminary ringing map and refined it by considering the property of ringing structure. The ringing detection of the proposed metric does not depend on edge detection, which is suitable for high degraded images. The characteristics of the ringing and blurring measures are analyzed and validated theoretically and experimentally. The performance of the proposed metric is tested and compared with that of some existing JPEG2000 sharpness metrics on three widely used databases. The experimental results show that the proposed metric is accurate and reliable in predicting the sharpness of JPEG2000 images.

Fast Depth Generating Algorithm From Binocular Images

2013 ◽  
Vol 284-287 ◽  
pp. 3102-3105
Author(s):  
Fang Hsuan Cheng ◽  
Tzu Hao Kuo
Keyword(s):  
Edge Detection ◽  
Line Segment ◽  
Stereo Matching ◽  
Color Difference ◽  
Experimental Results ◽  
Reference Image ◽  
Depth Information ◽  
High Quality ◽  
Line Segments

This paper proposes a line segment method to estimate the depth information from a pair of rectified images. The proposed method can achieve fast and high quality stereo-matching. This method first uses a simple edge detection to find out the line segments in the reference image and then calculates the color difference of each line segment from binocular images. The last step is to find out the minimum difference of each line segment as the corresponding points. From the experimental results, it is proved that the proposed method can fast and accurately generate the depth information from binocular images.

scholarly journals A retinal image sharpness metric based on histogram of edge width

2017 ◽  
Vol 11 (3) ◽  
pp. 292-300 ◽  
Author(s):  
Jia-Wen Lin ◽  
Qian Weng ◽  
Lan-Yan Xue ◽  
Xin-Rong Cao ◽  
Lun Yu
Keyword(s):  
Diabetic Retinopathy ◽  
Computational Complexity ◽  
Edge Detection ◽  
Retinal Image ◽  
Quality Evaluation ◽  
Region Of Interest ◽  
Fundus Image ◽  
Image Sharpness ◽  
Low Computational Complexity ◽  
Sharpness Metric

Retinal image sharpness assessment is one of the critical requirements of automatic quality evaluation in telemedicine screening for diabetic retinopathy. In this paper, a new sharpness metric measuring the spread of edges is presented to quantify fundus image clarity. After edge detection on the region of interest of retinal image, the width of each edge is calculated and the histogram of region of interest generated. Based on the histogram, a distance-based factor is introduced to gain the weighted edge width, which is defined as the sharpness metric for the fundus image. The method was tested on Messidor dataset and a proprietary dataset. The results show that the proposed metric performs well over different image distortion levels and resolutions and is of low computational complexity. The weighted edge width value of gradable retinal image, which is irrelevant to resolution, is always within the range of 3–7 pixels.

Devanagari Text Detection From Natural Scene Images

2020 ◽  
Vol 10 (3) ◽  
pp. 44-59
Author(s):  
Sankirti Sandeep Shiravale ◽  
R. Jayadevan ◽  
Sanjeev S. Sannakki
Keyword(s):  
Edge Detection ◽  
Image Understanding ◽  
Text Detection ◽  
Experimental Results ◽  
Combined Approach ◽  
Natural Scene ◽  
Light Conditions ◽  
The Individual ◽  
Natural Scene Images ◽  
Better Than

Text present in a camera captured scene images is semantically rich and can be used for image understanding. Automatic detection, extraction, and recognition of text are crucial in image understanding applications. Text detection from natural scene images is a tedious task due to complex background, uneven light conditions, multi-coloured and multi-sized font. Two techniques, namely ‘edge detection' and ‘colour-based clustering', are combined in this paper to detect text in scene images. Region properties are used for elimination of falsely generated annotations. A dataset of 1250 images is created and used for experimentation. Experimental results show that the combined approach performs better than the individual approaches.

scholarly journals Contour Detection for Fibre of Preserved Szechuan Pickle Based on Dilated Convolution

2019 ◽  
Vol 9 (13) ◽  
pp. 2684 ◽  
Author(s):  
Hongyang Li ◽  
Lizhuang Liu ◽  
Zhenqi Han ◽  
Dan Zhao
Keyword(s):  
Edge Detection ◽  
State Of The Art ◽  
Contour Detection ◽  
Experimental Results ◽  
Contour Method ◽  
Class Differences ◽  
Dilated Convolution ◽  
The Mean ◽  
Art Performance

Peeling fibre is an indispensable process in the production of preserved Szechuan pickle, the accuracy of which can significantly influence the quality of the products, and thus the contour method of fibre detection, as a core algorithm of the automatic peeling device, is studied. The fibre contour is a kind of non-salient contour, characterized by big intra-class differences and small inter-class differences, meaning that the feature of the contour is not discriminative. The method called dilated-holistically-nested edge detection (Dilated-HED) is proposed to detect the fibre contour, which is built based on the HED network and dilated convolution. The experimental results for our dataset show that the Pixel Accuracy (PA) is 99.52% and the Mean Intersection over Union (MIoU) is 49.99%, achieving state-of-the-art performance.

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