Image enhancement framework for low-resolution thermal images in visible and LWIR camera systems

2017 ◽  
Author(s):  
Thapanapong Rukkanchanunt ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Image Enhancement ◽  
Low Resolution ◽  
Thermal Images ◽  
Camera Systems

scholarly journals THE EFFECT OF IMAGE ENHANCEMENT METHODS DURING FEATURE DETECTION AND MATCHING OF THERMAL IMAGES

2017 ◽  
Vol XLII-1/W1 ◽  
pp. 575-578 ◽  
Author(s):  
O. Akcay ◽  
E. O. Avsar
Keyword(s):  
Image Enhancement ◽  
Digital Image ◽  
Image Matching ◽  
Feature Detection ◽  
Optical Resolution ◽  
Extraction Methods ◽  
Image Process ◽  
Low Resolution ◽  
Thermal Images ◽  
Speeded Up Robust Features

A successful image matching is essential to provide an automatic photogrammetric process accurately. Feature detection, extraction and matching algorithms have performed on the high resolution images perfectly. However, images of cameras, which are equipped with low-resolution thermal sensors are problematic with the current algorithms. In this paper, some digital image processing techniques were applied to the low-resolution images taken with Optris PI 450 382 x 288 pixel optical resolution lightweight thermal camera to increase extraction and matching performance. Image enhancement methods that adjust low quality digital thermal images, were used to produce more suitable images for detection and extraction. Three main digital image process techniques: histogram equalization, high pass and low pass filters were considered to increase the signal-to-noise ratio, sharpen image, remove noise, respectively. Later on, the pre-processed images were evaluated using current image detection and feature extraction methods Maximally Stable Extremal Regions (MSER) and Speeded Up Robust Features (SURF) algorithms. Obtained results showed that some enhancement methods increased number of extracted features and decreased blunder errors during image matching. Consequently, the effects of different pre-process techniques were compared in the paper.

scholarly journals Retinal Vasculature Extraction using Non-Subsampled Contourlet Transform and Multi-structure Element Morphology by Reconstruction

2019 ◽  
Vol 8 (2) ◽  
pp. 116-125
Keyword(s):  
Image Enhancement ◽  
Quantitative Measure ◽  
Image Database ◽  
Contourlet Transform ◽  
Retinal Vasculature ◽  
Low Resolution ◽  
Average Accuracy ◽  
Enhancement Method ◽  
Boosting Technique ◽  
Normal Inverse Gaussian

Retinal vasculature extraction is an area of utmost interest in ophthalmology. It helps to diagnose various diseases and also play a crucial role in treatment planning and accomplishment.In this work, we suggest an algorithm to segmentretinal vasculature fromretinal Fundus Images(FI) using multi-structure element morphology after enhancing the image using Normal Inverse Gaussian (NIG) model in the fuzzified Non-Subsampled Contourlet Transform (NSCT) domain. Since both noises and weak edges produce low magnitude NSCT coefficients, image enhancement methods amplify weak edges as well as noises. Direct application of image boosting technique in the NSCT domain causes over enhancement. So a novel image enhancement method is employed by interpreting the term “contrast” as a qualitative instead of a quantitative measure of the image. Membership values of NSCT coefficients are modified using NIG model. Mathematical Morphology(MM) by Multi-structure Elements (MEs) is used to extract the edges of image. False vessel ridges are expunged, and the thin vessel edges are preserved using opening by reconstruction. Connected component analysis followed by length filtering is used to filter the still remaining false edges. In most of the available literature, low-resolution fundus image databases are used for evaluating the algorithm. In our work, we evaluate our algorithm not only utilizing the DRIVE database, a low-resolution retinal image (RI) database, but also using an openly available High-Resolution Fundus (HRF) image database. Our result illustrates that the proposed method outperforms the other techniques considered with average accuracy (ACC) of 96.71%. In addition to ACC, we also use F1-Score and Mathews Correlation Coefficient (MCC) to evaluate our method. The average values of the results obtained with the HRF image database for F1-Score and MCC are 0.8172 and 0.8031, respectively, which are very much encouraging

Face recognition in low resolution thermal images

2013 ◽  
Vol 117 (12) ◽  
pp. 1689-1694 ◽  
Author(s):  
Eslam Mostafa ◽  
Riad Hammoud ◽  
Asem Ali ◽  
Aly Farag
Keyword(s):  
Face Recognition ◽  
Low Resolution ◽  
Thermal Images

scholarly journals Practical Application Possibilities for 3D Models Using Low-resolution Thermal Images

2021 ◽  
Vol 18 (4) ◽  
pp. 199-212
Author(s):  
András Molnár ◽  
István Lovas ◽  
Zsolt Domozi
Keyword(s):  
3D Models ◽  
Practical Application ◽  
Low Resolution ◽  
Thermal Images
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