In today’s information era, many news outlets are competing for attention. One way to cut through the noise is to use images. Obtaining images can be both time-consuming and expen- sive for smaller news agencies. In collaboration with the Swedish news agency Newsworthy, we investigate the possibilities of using AI-generated images in a journalistic context. Using images generated with the text-to-image generation model Stable Diffusion, we aim to answer the research question How do the parameters in Stable Diffusion affect the applicability of the generated images for journalistic purposes? A total of 511 images are generated with different Stable Diffusion parameter settings and rated on a scale of 1-5 by three journalists at Newswor- thy. The data is analyzed using ordinal logistic regression. The results suggest that the optimal value for the Stable Diffusion parameter classifier-free guidance is around 10-12, the default 50 iterations are sufficient, and keywords do not significantly affect the image outcome. The parameter that has the single greatest effect on the outcome is the prompt. Thus, to generate photo-realistic images that can be used in a journalistic context, most thought and effort should be put towards formulating a suitable prompt.

At the turn of the century, photographers started a transition from analogue to digital photography: a transition which took years to complete. The photographic landscape is now in a new period of development, with new AI-programs based on machine learning. These programs can be about (I) editing programs that aim to streamline the photographic workflow or correct pictures with technical flaws, such as reducing grain from a high ISO or enlarging pictures. The programs also incorporate (II) AI generated images based on prompting from an AI-artist, so called text-to-image programs like DALL-E 3, Midjourney, Stable Diffusion and Firefly 2. In this study we have done seven semi-structured interviews with Swedish photographers. We have used a postphenomenological theory based on Don Ihde’s philosophy. In analyzing the material from the interviews we have used narrative analysis. The results showed that photographers who worked in advertising, with organizations or clients had a higher tolerance for image manipulation with (II). These photographers valued effectiveness in their workflow and saw a larger need to adapt to new technology. While nature photographers valued authenticity and used (I) sparingly. Another result from the study was that Swedish photographers do not consider (II) photography and do not think that AI generated images should be allowed to compete in photo competitions, only in certain categories with only AI generated images.

Production of artificial Three-Dimension (3-D) images was the aim of many researches over hundreds of years. 3-D images are the images that create sense of depth when viewing them. 3-D images are closer to the real world scenes than 2-D images due to the 3-D effect or the sense of depth the 3-D images provide. Sense of depth can be caused by binocular cues including convergence and parallax. Convergence is created by the difference between the angles of the left eye and the right eye viewing axes. Parallax is the effect of viewing with one eye a view of the scene that is inherently shifted to the view seen by the other eye. Several techniques have targeted the creation of 3-D images with the mentioned cues. The technique is preferred when it is able to create 3-D images so that the viewer can view these images without wearing special glasses and the occurrence of viewer fatigue. Integral photography that was invented in 1908 is able to meet the previous requirements. Based on integral photography, several techniques, research and studies have been published. The purposes of this thesis include the computer simulation of flexible integral photography systems, the computer generation of good quality 3-D static and animated integral images using the simulated systems, optimising the generation process to be more accurate, less expensive, more effective, and faster, and producing a portable specialist software tool to achieve these targets. New techniques and algorithms are needed to meet these purposes. A literature survey was carried out about the closest researches and studies to the subject of computer-generated integral images; these were compared with the new techniques introduced in this study to prove the advantages and the necessity of these new techniques. The closest technique to the suggested techniques was implemented using more developed tools to compare the quality of the resulting integral images with the targeted integral images that are going to be produced using the tools and algorithms proposed in this thesis. A method to simulate an imaging system and produce integral images based on the new technique of dividing the view volume of the scene was introduced, explained, proved, and implemented with a program designed for this purpose. To optimise the processing time and the image quality, the previous method is developed, new features are added to the resulting integral images, and better performance was achieved by introducing the method of Displacing the Virtual Camera Target (DCT). Application software with Graphical User Interface is designed and implemented to allow users to select the required parameters of the imaging system and the required features of the resultant integral images. The software tool that is based on the developed techniques and employing OpenGL is useful to simulate the imaging systems, tune their parameters before the actual implementation of these systems, and as a result, save time and materials when designing these systems. The introduced techniques and the software tools are faster, more effective, and cheaper original methods to help in optimising both the integral imaging systems and the quality of integral images. These software tools based on the new techniques can be used on a wide range of devices and platforms because these are employing the portable Application Interface OpenGL. With these methods, integral imaging systems are simulated, and optimised; good quality static and animated integral images were created.

Artificiell intelligens (AI) tar alltmer plats i människors vardag. Ett område där det nyligen börjat användas i är bildgenerering och konstskapande. Då AI-tekniken inom bildgenerering är ny, har lite forskning gjorts kring hur det kan påverka konstnärer. Tidigare digitala framsteg som lett till verktyg som Photoshop har visats expandera hur konstnärer kan skapa konst och effektiviserat deras kreativa process. Bildgenererande AI, till skillnad från tidigare verktyg som Photoshop, kan generera egna bilder baserat på användarens indata. Detta väcker intresse kring förståelse om hur bildgenererande AI kommer potentiellt kunna påverka konstnärers kreativa process. Studiens mål är att utforska hur bildgenererande AI kan stötta konstnärer i den kreativa processen.  I studien användes en kvalitativ ansats för att fördjupa sig i ämnesområdet och besvara forskningsfrågan. En litteraturstudie som undersökte relevanta ämnen inom ämnesområdet utfördes vilket resulterade i tre aspekter, kontroll, samskapande och idégenerering. Kontroll var viktigt för stöttandet av användares nyttjande av AI. Samskapande och idégenerering var viktiga pelare som stöttar den kreativa processen. Dessa aspekter låg till grund för den empiriska studien. Den empiriska studien bestod av en sju dagars dagboksstudie där konstnärer fick prova tre olika typer av bildgenererande AI. Dagboksstudien kompletterades med intervjuer för att djupare förstå hur bildgenererande AI kunde stötta deras kreativa process. Studiens resultat visar på att bildgenererande AI kan användas för att stötta konstnärers kreativa process då det används som inspiration. Som tillägg identifierades tre förutsättningar som bör bemötas för att konstnärer ska kunna nyttja bildgenererande AI i deras kreativa process. / Artificial intelligence (AI) is increasingly becoming a part of people's daily lives. One area where it has recently been used is in image generation and art creation. As AI technology for image generation is new, little research has been done on how it may affect artists. Previous digital advancements, that led to tools such as Photoshop, have expanded how artists can create art and streamlined their creative process. Image-generating AI, unlike previous tools like Photoshop, can generate its own images based on user input. This raises interest in understanding how image-generating AI may potentially impact artists' creative process. The study aims to explore how image-generating AI can support artists in the creative process. The study used a qualitative approach to delve into the subject area and answer the research question. A literature review investigating relevant topics within the subject area was conducted, resulting in three aspects, control, co-creation, and idea generation. Control was important in supporting users' utilization of AI for their purposes. Co-creation and idea generation were important pillars that support the creative process. These aspects formed the basis for the empirical study. The empirical study consisted of a seven-day diary study where artists were given the opportunity to try three different types of image-generating AI. The diary study was complemented with interviews with the study participants to gain a deeper understanding of how image-generating AI could support their creative process. The study's results show that image-generating AI can be used to support artists' creative process when used as inspiration. In addition, three prerequisites were identified that need to be met for artists to be able to use image-generating AI in their creative process.