Potentially Achievable Image Definition in Video Applications

2020 ◽  
Author(s):  
Oleg Gofaizen ◽  
Olena Osharovska ◽  
Svitlana Kiiko ◽  
Mikola Patlayenko ◽  
Volodymyr Pyliavskyi ◽  
...  
Keyword(s):  
Image Definition

An Auto-Focus Algorithm of Fast Search Based on Combining Rough and Fine Adjustment

2012 ◽  
Vol 468-471 ◽  
pp. 534-537
Author(s):  
Zhao Hua Lin ◽  
Xin Liu ◽  
Yu Liang Zhang ◽  
Shu Mei Zhang
Keyword(s):  
Focal Plane ◽  
Narrow Range ◽  
Evaluation Function ◽  
Small Step ◽  
Uniform Sampling ◽  
Fast Search ◽  
Optical Measure ◽  
Fine Adjustment ◽  
Auto Focusing ◽  
Image Definition

A coarse and fine combined fast search and auto-focusing algorithm was suggested in this paper. This method can automatically search and find the focal plane by evaluating the image definition. The Krisch operator based edge energy function was used as the big-step coarse focusing, and then the wavelet transform based image definition evaluation function, which is sensitivity to the variation in image definition, was used to realize the small-step fine focusing in a narrow range. The un-uniform sampling function of the focusing area selection used in this method greatly reduces the workload and the required time for the data processing. The experimental results indicate that this algorithm can satisfy the requirement of the optical measure equipment for the image focusing.

Identification for Optical Image Definition Based on Edge Feature

2009 ◽  
Vol 36 (1) ◽  
pp. 172-176 ◽  
Author(s):  
倪军 Ni Jun ◽  
袁家虎 Yuan Jiahu ◽  
吴钦章 Wu Qinzhang
Keyword(s):  
Optical Image ◽  
Image Definition ◽  
Edge Feature

Validity of Definition Assessments for Motion Blur Identification

2011 ◽  
Vol 474-476 ◽  
pp. 1578-1582
Author(s):  
Shou Bing Xiang ◽  
Jin Ping He ◽  
Guang Da Su
Keyword(s):  
Experimental Data ◽  
Motion Blur ◽  
Linear Motion ◽  
Variable Parameters ◽  
Blur Identification ◽  
Image Definition

In this paper, based on the series of test images which are synthesized through the linear motion blur model with continue variable parameters, we define the sensitivity of image definition assessments. Experimental data are presented that the validity of definition assessments for motion blur identification closely relates to the sensitivity of definition assessments. The higher the sensitivity is, the better the validity of definition assessments for identification performs.

scholarly journals Brightness and image definition of pictures viewed from between the legs

2010 ◽  
Vol 73 (1) ◽  
pp. 144-159 ◽  
Author(s):  
Atsuki Higashiyama ◽  
Miyuki Toga
Keyword(s):  
Definition Of ◽  
Image Definition

A Thin Cloud Removal Method for Optical Image Based on Improved Homomorphism Filtering

2014 ◽  
Vol 618 ◽  
pp. 519-522
Author(s):  
Guang Yu ◽  
Wen Bang Sun ◽  
Gang Liu ◽  
Mai Yu Zhou
Keyword(s):  
High Frequency ◽  
Low Frequency ◽  
Frequency Component ◽  
Frequency Region ◽  
High Frequency Region ◽  
Filtering Method ◽  
Thin Cloud ◽  
Cloud Removal ◽  
Thin Cloud Removal ◽  
Image Definition

Optical remote image is affected by thin cloud inevitably, which debases image definition. Traditional homomorphism filtering frequently used in thin cloud removing has affect on the cloud in low frequency region, but is not effective for those in high frequency region. An improved homomorphism filtering method is proposed on the basis of statistical characters of image information. Instead of the filtering in frequency field, it isolates the low frequency component of the image representing cloud information with calculating neighborhood average in spatial field. Then, the filtered image is enhanced based on rough set. The experiment results show that the proposed method compared to traditional methods can obtain good results and performs faster.

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