We propose an efficient digital simulation of film grain noise that is based on the random-dot model, applies to a wide range of imaging conditions (magnification factors, optical blur), and accounts for the signal-dependence and spatial correlation of film grain. Our analysis and experimental results demonstrate that the proposed approach produces sharper images with similar film grain compared to Monte Carlo simulations proposed by Newson et al., offering superior rendering efficiency across a wide range of parameter settings including a case where Monte Carlo simulations fail (low magnification with low blur). While recently proposed statistical approximations of film grain by Zhang et al. are significantly faster, they cannot be used for high magnification or low blur.

Traditional frame-based imaging systems face trade-offs especially under low-light conditions: long-exposure frames have high signal-to-noise ratio (SNR) but suffer from motion blur and low frame rate, while short-exposure frames are sharp but suffer from noise. Event cameras can complement these modalities by capturing brightness changes and exposure-invariant information at very high temporal resolution. We propose a novel multi-modal framework for lowlight enhancement by integrating information from long-exposure, short-exposure, and event data. We introduce an enhanced lightup mechanism to improve short-exposure frames using illumination from long-exposure frames, and an SNR-guided fusion strategy for noise suppression and detail preservation. We use event information to improve feature selection, especially in the low SNR regions. Experimental results on public datasets demonstrate that our method can effectively handle noise and motion blur. We obtain 14dB improvement in PSNR over state-of-the-art methods based on conventional frames and 4dB improvement over methods based on frames and events.

We propose a realistic film grain rendering algorithm based on statistics derived analytically from a physics-based Boolean model that Newson et al. adopted for Monte Carlo simulations of film grain. We also propose formulas for estimation of the model parameters from scanned film grain images. The proposed rendering is computationally efficient and can be used for real-time film grain simulation for a wide range of film grain parameters when the individual film grains are not visible. Experimental results demonstrate the effectiveness of the proposed approach for both constant and real-world images, for a six orders of magnitude speed-up compared with the Monte Carlo simulations of the Newson et al. approach.

We present a pattern-based approach for reconstructing a K-level image from cutsets, dense samples taken along a family of lines or curves in two- or three-dimensional space, which break the image into blocks, each of which is typically reconstructed independently of the others. The pattern-based approach utilizes statistics of human segmentations to generate a codebook of patterns, each of which represents a pair of a block boundary specification and the corresponding pattern in the block interior. We develop the approach for rectangular cutset topologies and show that it can be extended to general periodic sampling topologies. We also show that, for bilevel cutset reconstruction, the pattern-based can be combined with the previously proposed cutset-MRF approach to substantially reduce the size of the codebook with a slight increase in reconstruction error. In addition, we present an algorithm for segmenting the cutset samples of an original grayscale or color image, followed by reconstruction of the full segmentation field via the pattern-based approach. Experimental results show that the proposed approaches outperform the cutset-MRF approaches in terms of both reconstruction error rate and perceptual quality. Moreover, this is accomplished without any side information about the structure of the block interior. Systematic comparisons of the performance of different sampling topologies are also provided.