Noise Modelling for Smartphone Cameras

In our everyday life we capture photographs. We create these images by measuring the amount of light, radiated from scenes in our physical world, on camera sensors embedded in our smartphones. Image noise is variation in the measurement of intensities or colours in digital images and it has the undesirable effect of obscuring information in images. Image noise is produced from two main sources: (1) the unavoidable, random nature of light and (2) the imaging sensor and associated circuitry. Unlike professional cameras, smartphone cameras have much smaller imaging sensors which makes them more susceptible to higher and more complex noise. To model, and ultimately remove, image noise, many mathematical models have been proposed. These models either represent synthetic noise or rely on assumptions that makes them unable to model real noise distributions observed from empirical data. One major reason for that is the lack of sufficient real noisy image datasets with ground truth images that can enable the study of real camera noise. The purpose of this dissertation is to provide a study on image noise modelling based on data-driven approaches specific to smartphone cameras. To this end, we first propose a systematic method for estimating ground truth noise-free images from noisy images captured by smartphone cameras. Using the proposed method, we collect a large-scale dataset, termed the Smartphone Image Denoising Dataset (SIDD), of high-quality images that can be used for noise modelling. Next, we utilize the SIDD dataset to devise a generative noise model, termed Noise Flow, that can be used to synthesise realistic noisy images to be utilized in many computer vision tasks. We also use our datatset to provide a benchmark for image denoising algorithms on real noisy images. As part of this benchmarking effort, we have developed an online image denoising challenge with the necessary software tools to facilitate the evaluation of image denoising methods applied to realistic noisy images. We believe the work in this dissertation helps to advance the state of the art in image noise modelling and image denoising.