Multiframe image superresolution has been an active research area for many years. In this approach image processing techniques are used to combine multiple low-resolution (LR) images capturing different views of an object. These multiple images are generally under-sampled, degraded by optical and pixel blurs, and corrupted by measurement noise. We exploit diversities in the imaging channels, namely, the number of cameras, magnification, position, and rotation, to undo degradations. Using an iterative back-projection (IBP) algorithm we quantify the improvements in image fidelity gained by using multiple frames compared to single frame, and discuss effects of system parameters on the reconstruction fidelity. As an example, for a system in which the pixel size is matched to optical blur size at a moderate detector noise, we can reduce the reconstruction root-mean-square-error by 570% by using 16 cameras and a large amount of diversity in deployment.We develop a new technique for superresolving binary imagery by incorporating finite-alphabet prior knowledge. We employ a message-passing based algorithm called two-dimensional distributed data detection (2D4) to estimate the object pixel likelihoods. We present a novel complexity-reduction technique that makes the algorithm suitable even for channels with support size as large as 5x5 object pixels. We compare the performance and complexity of 2D4 with that of IBP. In an imaging system with an optical blur spot matched to pixel size, and four 2x2 undersampled LR images, the reconstruction error for 2D4 is 300 times smaller than that for IBP at a signal-to-noise ratio of 38dB.We also present a transform-domain superresolution algorithm to efficiently incorporate sparsity as a form of prior knowledge. The prior knowledge that the object is sparse in some domain is incorporated in two ways: first we use the popular L1 norm as the regularization operator. Secondly we model wavelet coefficients of natural objects using generalized Gaussian densities. The model parameters are learned from a set of training objects and the regularization operator is derived from these parameters. We compare the results from our algorithms with an expectation-maximization (EM) algorithm for L1 norm minimization and also with the linear minimum mean squared error (LMMSE) estimator.

Digitization of physical artwork is usually done using image scanning devices in order to ensure that the output is accurate in terms of color and is of sufficiently high resolution, usually over 300 pixels per inch, however the usage of such a device is in some cases unfeasible due to medium or size constraints. Photography of the artwork is another method of artwork digitization, however such methods often produce results containing camera artifacts such as shadows, reflections or low resolution. This thesis project explores the possibility of creating an alternative to image scanners using smartphone photography and machine learning-based methods. Due to the very high memory requirement for enhancing images at very high resolutions, this is done in a two-stage process. The first stage uses an unpaired image style transfer model to remove shadows and highlights. The second stage uses a superresolution model to increase the resolution of the image. The results are evaluated on a small set of paired images using objective metrics and subjective metrics in the form of a user study. In some cases the method removed camera artifacts in the form of reflection and color accuracy, however the best results were achieved when the input data did not contain any major camera artifacts. Based on this it seems likely that style transfer models are not applicable for problems with a wide range of expected input and output. The use of super-resolution seems to be a crucial component of high-resolution image enhancement and the current state-of-the-art methods are able to convincingly increase the resolution of images provided that the input is of a sufficiently high resolution. The subjective evaluation shows that commonly used metrics such as structural similarity and Fréchet Inception Distance are applicable for this type of problem when analyzing the full image, however for smaller details other evaluation methods are required. / Digitalisering av fysiska konstverk görs vanligtvis med bildskannrar för att försäkra att den digitaliserade bilden är färgnoggrann och att upplösningen är tillräckligt hög, vanligtvis över 300 pixlar per tum. Dock är användandet av bildskannrar ibland svårt på grund av konstverkets material eller storlek. Fotografi av konstverk är en annan metod för digitalisering, men denna metod producerar ofta kameraartefakter i form av skuggor, reflektioner och låg upplösning. Detta examensarbete utforskar möjligheten att skapa ett alternativ till bildskannrar genom att använda smartphonefotografi och maskininlärningsbaserade metoder. På grund av de höga minneskraven för bildförbättring med mycket höga upplösningar görs detta i en tvåstegsprocess. Det första steget använder oparad bildstilöversättning för att eliminera skuggor och ljuspunkter. Det andra steget använder en superupplösningsmodell för att öka bildens upplösning. Resultaten utvärderas på en liten mängd parade bilder med objektiva jämförelser och subjektiva jämförelser i form av en användarstudie. I vissa fall reducerade metoden kameraartefakter i form av reflektioner och förbättrade färgexakthet, dock skedde dessa resultat i fall där indatan inte innehöll några större kameraartefakter. Baserat på detta är det sannolikt att stilöversättningsmodeller inte är applicerbara för problem med ett brett omfång av möjliga indata och utdata. Användandet av superupplösning verkar vara en viktig komponent av högupplöst bildförbättring och de bäst presenterande metoderna kan övertygande öka upplösningen av bilder i fall där indatan är av tillräckligt hög upplösning. Den subjektiva utvärderingen visar att vanligt använda utvärderingsmetoder som Fréchet-Inception-avstånd och strukturell likhet är applicerbara för denna typ av problem när de används för att analysera en hel bild, men för mindre detaljer behövs alternativa utvärderingsmetoder.