A Novel Image Similarity Measure Based on Greatest and Smallest Eigen Fuzzy Sets

A novel image similarity index based on the greatest and smallest fuzzy set solutions of the max–min and min–max compositions of fuzzy relations, respectively, is proposed. The greatest and smallest fuzzy sets are found symmetrically as the min–max and max–min solutions, respectively, to a fuzzy relation equation. The original image is partitioned into squared blocks and the pixels in each block are normalized to [0, 1] in order to have a fuzzy relation. The greatest and smallest fuzzy sets, found for each block, are used to measure the similarity between the original image and the image reconstructed by joining the squared blocks. Comparison tests with other well-known image metrics are then carried out where source images are noised by applying Gaussian filters. The results show that the proposed image similarity measure is more effective and robust to noise than the PSNR and SSIM-based measures

Drift Correction for Scanning-Electron Microscopy by

Scanning electron micrographs at high magnification (100,000x and up) are distorted by motion of the sample during image acquisition, a phenomenon called drift. We propose a method for correcting drift distortion in images obtained on scanning electron and other scanned-beam microscopes by registering a series of images to create a drift-free composite. We develop a drift-distortion model for linear drift and use it as a basis for an affine correction between images in the sequence. The performance of our correction method is evaluated with simulated datasets and real datasets taken on both scanning electron and scanning helium-ion microscopes; we compare performance against translation only correction. In simulation, we exhibit a 12.5 dB improvement in SNR of our drift-corrected composite compared to a non-aligned composite, and a 3 dB improvement over translation correction. A more modest 0.

Drift correction for scanning-electron microscopy

Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2010.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Cataloged from student submitted PDF version of thesis.Includes bibliographical references (p. 91-92).Scanning electron micrographs at high magnification (100,000x and up) are distorted by motion of the sample during image acquisition, a phenomenon called drift. We propose a method for correcting drift distortion in images obtained on scanning electron and other scanned-beam microscopes by registering a series of images to create a drift-free composite. We develop a drift-distortion model for linear drift and use it as a basis for an affine correction between images in the sequence. The performance of our correction method is evaluated with simulated datasets and real datasets taken on both scanning electron and scanning helium-ion microscopes; we compare performance against translation only correction. In simulation, we exhibit a 12.5 dB improvement in SNR of our drift-corrected composite compared to a non-aligned composite, and a 3 dB improvement over translation correction. A more modest 0.4 dB improvement is measured on the real image sets compared to translation correction alone.by Michael T. Snella.M.Eng

Investigating Polynomial Fitting Schemes for Image Compression

Image compression is a means to perform transmission or storage of visual data in the most economical way. Though many algorithms have been reported, research is still needed to cope with the continuous demand for more efficient transmission or storage. This research work explores and implements polynomial fitting techniques as means to perform block-based lossy image compression. In an attempt to investigate nonpolynomial models, a region-based scheme is implemented to fit the whole image using bell-shaped functions. The idea is simply to view an image as a 3D geographical map consisting of hills and valleys. However, the scheme suffers from high computational demands and inferiority to many available image compression schemes. Hence, only polynomial models get further considerations. A first order polynomial (plane) model is designed to work in a multiplication- and division-free (MDF) environment. The intensity values of each image block are fitted to a plane and the parameters are then quantized and coded. Blocking artefacts, a common drawback of block-based image compression techniques, are reduced using an MDF line-fitting scheme at blocks’ boundaries. It is shown that a compression ratio of 62:1 at 28.8dB is attainable for the standard image PEPPER, outperforming JPEG, both objectively and subjectively for this part of the rate-distortion characteristics. Inter-block prediction can substantially improve the compression performance of the plane model to reach a compression ratio of 112:1 at 27.9dB. This improvement, however, slightly increases computational complexity and reduces pipelining capability. Although JPEG2000 is not a block-based scheme, it is encouraging that the proposed prediction scheme performs better in comparison to JPEG 2000, computationally and qualitatively. However, more experiments are needed to have a more concrete comparison. To reduce blocking artefacts, a new postprocessing scheme, based on Weber’s law, is employed. It is reported that images postprocessed using this scheme are subjectively more pleasing with a marginal increase in PSNR (<0.3 dB). The Weber’s law is modified to perform edge detection and quality assessment tasks. These results motivate the exploration of higher order polynomials, using three parameters to maintain comparable compression performance. To investigate the impact of higher order polynomials, through an approximate asymptotic behaviour, a novel linear mapping scheme is designed. Though computationally demanding, the performances of higher order polynomial approximation schemes are comparable to that of the plane model. This clearly demonstrates the powerful approximation capability of the plane model. As such, the proposed linear mapping scheme constitutes a new approach in image modeling, and hence worth future consideration

Gradient-based image and video quality assessment

У овој дисертацији разматране су објективне мере процене квалитета слике и видеа са потпуним и делимичним референцирањем на изворни сигнал. За потребе евалуације квалитета развијене су поуздане, рачунски ефикасне мере, засноване на очувању информација о градијенту. Мере су тестиране на великом броју тест слика и видео секвенци, различитих типова и степена деградације. Поред јавно доступних база слика и видео секвенци, за потребе истраживања формиране су и нове базе видео секвенци са преко 300 релевантних тест узорака. Поређењем доступних субјективних и објективних скорова квалитета показано је да је објективна евалуација квалитета веома сложен проблем, али га је могуће решити и доћи до високих перформанси коришћењем предложених мера процене квалитета слике и видеа.U ovoj disertaciji razmatrane su objektivne mere procene kvaliteta slike i videa sa potpunim i delimičnim referenciranjem na izvorni signal. Za potrebe evaluacije kvaliteta razvijene su pouzdane, računski efikasne mere, zasnovane na očuvanju informacija o gradijentu. Mere su testirane na velikom broju test slika i video sekvenci, različitih tipova i stepena degradacije. Pored javno dostupnih baza slika i video sekvenci, za potrebe istraživanja formirane su i nove baze video sekvenci sa preko 300 relevantnih test uzoraka. Poređenjem dostupnih subjektivnih i objektivnih skorova kvaliteta pokazano je da je objektivna evaluacija kvaliteta veoma složen problem, ali ga je moguće rešiti i doći do visokih performansi korišćenjem predloženih mera procene kvaliteta slike i videa.This thesis presents an investigation into objective image and video quality assessment with full and reduced reference on original (source) signal. For quality evaluation purposes, reliable, computational efficient, gradient-based measures are developed. Proposed measures are tested on different image and video datasets, with various types of distorsions and degradation levels. Along with publicly available image and video quality datasets, new video quality datasets are maded, with more than 300 relevant test samples. Through comparison between available subjective and objective quality scores it has been shown that objective quality evaluation is highly complex problem, but it is possible to resolve it and acchieve high performance using proposed quality measures