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abstract: The depth richness of a scene translates into a spatially variable defocus blur in the acquired image. Blurring can mislead computational image understanding; therefore, blur detection can be used for selective image enhancement of blurred regions and the application of image understanding algorithms to sharp regions. This work focuses on blur detection and its application to image enhancement. This work proposes a spatially-varying defocus blur detection based on the quotient of spectral bands; additionally, to avoid the use of computationally intensive algorithms for the segmentation of foreground and background regions, a global threshold defined using weak textured regions on the input image is proposed. Quantitative results expressed in the precision-recall space as well as qualitative results overperform current state-of-the-art algorithms while keeping the computational requirements at competitive levels. Imperfections in the curvature of lenses can lead to image radial distortion (IRD). Computer vision applications can be drastically affected by IRD. This work proposes a novel robust radial distortion correction algorithm based on alternate optimization using two cost functions tailored for the estimation of the center of distortion and radial distortion coefficients. Qualitative and quantitative results show the competitiveness of the proposed algorithm. Blur is one of the causes of visual discomfort in stereopsis. Sharpening applying traditional algorithms can produce an interdifference which causes eyestrain and visual fatigue for the viewer. A sharpness enhancement method for stereo images that incorporates binocular vision cues and depth information is presented. Perceptual evaluation and quantitative results based on the metric of interdifference deviation are reported; results of the proposed algorithm are competitive with state-of-the-art stereo algorithms. Digital images and videos are produced every day in astonishing amounts. Consequently, the market-driven demand for higher quality content is constantly increasing which leads to the need of image quality assessment (IQA) methods. A training-free, no-reference image sharpness assessment method based on the singular value decomposition of perceptually-weighted normalized-gradients of relevant pixels in the input image is proposed. Results over six subject-rated publicly available databases show competitive performance when compared with state-of-the-art algorithms. / Dissertation/Thesis / Doctoral Dissertation Electrical Engineering 2019

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Teknologiska framsteg påverkar tillverkningsindustrin genom att integrera tester för kretskortens status, lödning och elektriska egenskaper. En pick-and-place-maskin används för att verifiera komponentpositioner på kretskortet, men underhåll och re-paration av dessa maskiner medför stora kostnader. Industry 4.0 introducerar auto-matiseringslösningar som Internet of Things, Big Data, dataanalys och cybersäker-het, vilket kräver uppgradering av utrustning. Att erbjuda prisvärda uppgraderingar för äldre maskiner är avgörande för små och medelstora företags konkurrenskraft. Validering av kretskortkomponenterna på ett kostnadseffektiv och tillförlitligt sätt kan optimera kretskortsindustri.Det finns idag framgångsrika metoder för att lösa liknande problem. Konvolutionella neurala nätverk, finkornig bildigenkänning, funktionsmatchning samt funktions-extraktion är exempel på dessa. För att verifiera korrekt positionering av en krets-kortskomponent utnyttjas en automatisk maskin som systematiskt inspekterar kretskortet med hjälp av en pick-and-place fil som inkluderar exakta koordinater för varje position.Denna studie använder bildigenkänning för att finna en ekonomisk implementerbar lösning på identifiering av kretskortskomponenter. Detta för att undersöka om mo-biltelefonkameran är en möjlig implementationslösning för små till medelstora före-tag att identifiera kretskort. En mobilkamera användes för att ta bilder på kretskor-ten. Detta gjordes i varierande ljussättning, vinklar samt avstånd från kretskortet. En algoritm skapades för att undersöka ifall komponenterna är korrekt placerade på kretskortet. Segmentering, morfologiska processer och kunskapsbaserade metoder används i denna algoritm.Resultatet tyder på att det är en lämplig lösning att använda bildbehandling för att identifiera kretskortskomponenters position. Att identifiera korrekt satta eller fel-aktigt satta komponenter är möjligt förutsatt att bilderna är tagna med referensnära inställningar. Därav kan små till medelstora företag använda sig utav denna lösning för att få en kostnadseffektiv samt tillförlitlig identifiering. / Technological advances are impacting the manufacturing industry by integrating tests for circuit board status, soldering, and electrical properties. A pick-and-place machine is used to verify components positions on the circuit board, but mainte-nance and repair of these machines incurs large costs. Industry 4.0 introduces auto-mation solutions such as the Internet of Things, Big Data, data analysis, and cyberse-curity, which require equipment upgrades. Offering affordable upgrades for older machines is critical to the competitiveness of small and medium-sized businesses. Validation of the circuit board components in a cost-effective and reliable way can optimize the circuit board industry.Today there are successful methods of solving similar problems. Convolutional neu-ral networks, fine-grained image recognition, feature matching, and feature extrac-tion are examples of these. To verify the correct positioning of a circuit board com-ponent, an automatic machine is used that systematically inspects the circuit board using a pick-and-place file that includes exact coordinates for each position.This study uses image recognition to find an economically implementable solution to the identification of printed circuit board components. This is to investigate whether the mobile phone camera is a possible implementation solution for small to me-dium-sized companies to identify circuit boards. The mobile phone camera was used to capture images of the circuit board. This was done in varying lighting, angles, and distance from the circuit board. An algorithm was created to check if the compo-nents are correctly placed on the circuit board. Segmentation, morphological pro-cesses, and knowledge-based methods are used in this algorithm.The result suggests that using image processing to identify the position of circuit board components is a suitable solution. Identifying correctly set or incorrectly set components is possible provided that the images are taken with reference-close set-tings. Therefore, small to medium-sized companies can use this solution to obtain cost-effective and reliable identification.