This dissertation focuses on two distinct but related problems involving detection in multiple images. The first problem focuses on the accurate detection of runways by fusing Synthetic Vision System (SVS) and Enhanced Vision System (EVS) images. A novel procedure is developed to accurately detect runways and horizons and also enhance runway surrounding areas by fusing enhanced vision system (EVS) and synthetic vision system (SVS) images of the runway while an aircraft is landing. Because the EVS and SVS frames are not aligned, a registration step is introduced to align the EVS and SVS images prior to fusion. The most notable feature of the registration procedure is that it is guided by the information extracted from the weather-invariant SVS images. Four fusion rules based on combining Discrete Wavelet Transform (DWT) sub-bands are implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and also on image pairs containing simulated EVS images with varying levels of turbulence. The subjective and objective evaluations reveal that runways and horizons can be detected accurately even in poor visibility conditions. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. Another notable feature is that the entire procedure is autonomous throughout the landing sequence irrespective of the weather conditions. Given the excellent fusion results and the autonomous feature, it can be concluded that the fusion procedure developed is quite promising for incorporation into head-up displays (HUDs) to assist pilots in safely landing aircrafts in varying weather conditions. The second problem focuses on the blind detection of hidden messages that are embedded in images using various steganography methods. A new steganalysis strategy is introduced to blindly detect hidden messages that have been embedded in JPEG images using various steganography techniques. The key contribution is the formulation of a multi-domain feature extraction, ranking, and selection strategy to improve the steganalysis performance. The multi-domain features are statistical measures extracted from DWT, muti-wavelet (MWT), and slantlet (SLT) transforms. Feature ranking and selection is based on evaluating the performance of each feature independently and combining the best uncorrelated features. The resulting feature set is used in conjunction with discriminant analysis and support vector classifiers to detect the presence/absence of hidden messages in images. Numerous experiments are conducted to demonstrate the improved performance of the new steganalysis strategy over existing steganalysis methods.

Segmentation of ultrasonic images is a common task in healthcare that requires time and attention from healthcare professionals. Automation of medical image segmentation using deep learning solutions is fast growing field and has been shown to be capable of near human performance. Ultrasonic images suffer from low signal-to-noise ratio and speckle patterns, noise filtering is a common pre-processing step in non-deep learning image segmentation methods used to improve segmentation results. In this thesis the effect of speckle filtering of echocardiographic images in deep learning segmentation using U-Net is investigated. When trained with speckle reduced and despeckled datasets, a U-Net model with 0.5·106 trainable parameters saw an rage dice score improvement of +0.15 in the 17 out of 32 categories that were found to be statistically different compared to the same network trained with unfiltered images. The U-Net model with 1.9·106 trainable parameters saw a decrease in performance in only 5 out of 32 categories, and the U-Net model with 31·106 trainable parameters saw a decrease in performance in 10 out of 32 categories when trained with the speckle filtered datasets. No definite differences in performance between the use of speckle suppression and full speckle removal were observed. This result shows potential for speckle filtering to be used as a means to reduce the complexity required of deep learning models in ultrasound segmentation tasks. The use of the wavelet transform as a down- and up-sampling layer in U-Net was also investigated. The speckle patterns in ultrasonic images can contain information about the tissue. The wavelet transform is capable of lossless down- and up-sampling in contrast to the commonly used down-sampling methods, which could enable the network to make use textural information and improve segmentations. The U-Net modified with the wavelet transform shows slightly improved results when trained with despeckled datasets compared to the unfiltered dataset, suggesting that it was not capable of extracting any information from the speckle. The experiments with the wavelet transform were far from exhaustive and more research is needed for proper assessment. / Segmentering av ultraljudsbilder är en vanlig uppgift inom vården som kräver tid och uppmärksamhet från vårdpersonal. Automatisering av medicinsk bildsegmentering med djupinlärning är ett snabbt växande område och har visat kunna nå prestanda nära mänsklig nivå.  Ultraljudsbilder har dålig signal-brusförhållande och speckle mönster, ofta bearbetas bilder med brusfiltrering när icke djupinlärningsmetoder används för segmentering för att förbättra resultat. Effekten av speckle-filtrering i ultraljudsbilder i djupinlärnings segmentering med U-Net undersöks i den här masterexamensuppsatsen.   U-Net nätverket med 0.5·106 träningsbara parametrar presterade bättre när den tränades med speckle filtrerade dataset jämfört för med ofiltrerade bilder, men en ökning i dice-koefficienten av +0.15 i medel i de 17 kategorier av 32 som var statistikst signifikanta. En försämring av resultaten för U-Net nätverket med 1.9·106 träningsbara parametrar observerades i 5 av 32 kategorier, och en försämring av resultaten för U-Net nätverket med 31·106 träningsbara parametrar observerardes när de tränades med speckle filtrerade dataset i 10 av 32 kategorier. Inga skillnader i prestanda mellan användning av minskning av speckle och fullständig speckle borttagning observerades. Detta resultat visar att det finns potential för att använda speckle filtrering som en metod för att minska komplexiteten som kan krävas hos djupinlärningsnätverk inom ultraljudssegmentering. Användning av wavelet transformen som ett ned- och uppsamplings lager i U-Net undersöktes också. Speckle mönstren i ultraljudsbilder kan innehålla information om vävnaden. Wavelet transformen möjliggör ned- och uppsamplings av bilden utan informationsförlust till skillnad från de vanliga metoderna, vilket skulle kunna göra det möjligt för nätverket att utnyttja information om vävnadstexturen och förbättra segmenteringarna. U-Net nätverket som modifierades med wavelet transformen visar någorlunda bättre prestanda när den tränas med speckle filtrerade dataset jämfört med ofiltrerade dataset. Det tyder på att nätverket inte kunde utnyttja någon information från speckle mönstren. Wavelet transform experimenten var ej uttömmande och mer forskning behövs för en korrekt bedömning.