Poboljšanje slike i vrednovanje radnih značajki korištenjem raznih filtara na daljinski mjerene podatke IRS-P6 satelita Liss IV

This paper presents fast and effective filtering techniques for image enhancement from remote sensing Indian remote sensing satellite P6 Liss IV remotely sensed data like Near-Infrared band. There are four filtering techniques used for image enhancement based on spatial domain filters and frequency domain filters such as median filter, wiener filter, bilateral filter and Gaussian homomorphic filter and selected noises salt and pepper and Gaussian noise used with filter. Selected images tested with each filter and based on PSNR performance metric value and best filtering technique identified from these filters. Finally, Gaussian homomorphic filtering technique is suitable for image enhancement of the Liss IV remotely sensed Near-Infrared band. Image enhancement technique is preprocessing for future work such as edge detection and image segmentation.U radu su prikazane brze i učinkovite tehnike filtriranja za poboljšanje slike iz podataka u bliskom infracrvenom području dobivenih indijskim satelitom za daljinska istraživanja P6 Liss IV. Korištene su četiri tehnike filtriranja temeljene na filtrima u prostornoj i frekvencijskoj domeni kao što su: medijan filtar, Wiener filtar, bilateralni filtar i gaussovski homomorfni filtar uz odabrane šumove “salt and pepper” i gaussovski šum s filtrom. Odabrane slike testirane su sa svakim od filtera te je na temelju metričke vrijednosti PSNR (Peak Signal Noise Ratio) radne značajke prepoznata najbolja tehnika filtriranja. Konačno se pokazalo da je gaussovska homomorfna tehnika filtriranja prikladna za poboljšanje slika dobivenih pomoću satelita Liss IV u bliskom infracrvenom području. Tehnika poboljšanja slike je predobrada za budući rad, kao što je detekcija ruba i segmentacija slike

Infrared Image Enhancement Using Wavelet Transform

In Infrared Image Enhancement using Wavelet Transform, two enhancement algorithms namely spatial and spatiotemporal homomorphic filtering (SHF and STHF) have been given for enhancement of the far infrared images based upon a far infrared imaging model. Although spatiotemporal homomorphic filtering may reduce the number of iterations greatly in comparison to spatial one for a similar degree of convergence by making explicit use of the additional information provided temporally, the enhanced results from SHF are in general better than those from STHF. In this dissertation work an additive wavelet transform will be proposed for enhancement and filtration of homomorphic infrared images. Keywords: Infrard Images, Additive Wavelet transform, Homomorphic Image Enhancement

Frequency and Spatial Domains Adaptive-based Enhancement Technique for Thermal Infrared Images

Low contrast and noisy image limits the amount of information conveyed to the user. With the proliferation of digital imagery and computer interface between man-and-machine, it is now viable to consider digital enhancement in the image before presenting it to the user, thus increasing the information throughput. With better contrast, target detection and discrimination can be improved. The paper presents a sequence of filtering operations in frequency and spatial domains to improve the quality of the thermal infrared (IR) images. Basically, two filters – homomorphic filter followed by adaptive Gaussian filter are applied to improve the quality of the thermal IR images. We have systematically evaluated the algorithm on a variety of images and carefully compared it with the techniques presented in the literature. We performed an evaluation of three filter banks such as homomorphic, Gaussian 5×5 and the proposed method, and we have seen that the proposed method yields optimal PSNR for all the thermal images. The results demonstrate that the proposed algorithm is efficient for enhancement of thermal IR images.Defence Science Journal, Vol. 64, No. 5, September 2014, pp.451-457, DOI:http://dx.doi.org/10.14429/dsj.64.687

Pigment Melanin: Pattern for Iris Recognition

Recognition of iris based on Visible Light (VL) imaging is a difficult problem because of the light reflection from the cornea. Nonetheless, pigment melanin provides a rich feature source in VL, unavailable in Near-Infrared (NIR) imaging. This is due to biological spectroscopy of eumelanin, a chemical not stimulated in NIR. In this case, a plausible solution to observe such patterns may be provided by an adaptive procedure using a variational technique on the image histogram. To describe the patterns, a shape analysis method is used to derive feature-code for each subject. An important question is how much the melanin patterns, extracted from VL, are independent of iris texture in NIR. With this question in mind, the present investigation proposes fusion of features extracted from NIR and VL to boost the recognition performance. We have collected our own database (UTIRIS) consisting of both NIR and VL images of 158 eyes of 79 individuals. This investigation demonstrates that the proposed algorithm is highly sensitive to the patterns of cromophores and improves the iris recognition rate.Comment: To be Published on Special Issue on Biometrics, IEEE Transaction on Instruments and Measurements, Volume 59, Issue number 4, April 201

Multi-resolution analysis for region of interest extraction in thermographic, nondestructive evaluation

Infrared Non-Destructive Testing (INDT) is known as an effective and rapid method for nondestructive inspection. It can detect a broad range of near-surface structuring flaws in metallic and composite components. Those flaws are modeled as a smooth contour centered at peaks of stored thermal energy, termed Regions of Interest (ROI). Dedicated methodologies must detect the presence of those ROIs. In this paper, we present a methodology for ROI extraction in INDT tasks. The methodology deals with the difficulties due to the non-uniform heating. The non-uniform heating affects low spatial/frequencies and hinders the detection of relevant points in the image. In this paper, a methodology for ROI extraction in INDT using multi-resolution analysis is proposed, which is robust to ROI low contrast and non-uniform heating. The former methodology includes local correlation, Gaussian scale analysis and local edge detection. In this methodology local correlation between image and Gaussian window provides interest points related to ROIs. We use a Gaussian window because thermal behavior is well modeled by Gaussian smooth contours. Also, the Gaussian scale is used to analyze details in the image using multi-resolution analysis avoiding low contrast, non-uniform heating and selection of the Gaussian window size. Finally, local edge detection is used to provide a good estimation of the boundaries in the ROI. Thus, we provide a methodology for ROI extraction based on multi-resolution analysis that is better or equal compared with the other dedicate algorithms proposed in the state of art

Vehicle Combustion Quality Monitoring:A scene visibility-level based non-invasive approach

Pollutants interfere with light, restrict its reflection and so impair visibility. Scene visibility level is therefore used as a measure of air quality and pollution. Treating emission efflux as "some additional noise causing visibility impairment," this work examines if the extracted visibility index from a thermal infrared (TIR) image can help in qualitative assessment of combustion efficiency. The thin-film regime like two dimensional TIR images of unleaded-petroleum run vehicles' exhaust-plumes were first accommodated for time and space related compositional effects. The estimated ratios of visibility indices obtained from two sequential TIR images of the same exhaust plume were compared with their respective electrochemically sensed levels of oxides of nitrogen and combustibles. Initial results suggest that visibility indices extracted from TIR images of emission efflux would help in distinguishing low from high levels of emissions. TIR images can therefore assist in qualitative assessment of engine combustion efficiency

A Fuzzy Homomorphic Algorithm for Image Enhancement

The implementation and analysis of a novel Fuzzy Homomorphic image enhancement technique is presented. The technique combines the logarithmic transform with fuzzy membership functions to deliver an intuitive method of image enhancement. This algorithm reduces the computational complexity by eliminating the need for image-size-dependent filter kernels and the forward and inverse Fourier Transforms.   The proposed algorithm is compared with the more established algorithms for the enhancement of low contrast images with uneven illumination. The results show that the fuzzy method provides similar or better results than the frequency domain method and some other well-known image enhancement algorithms