In the dynamic realm of image processing, coordinate-based neural networks have made significant strides, especially in tasks such as 3D reconstruction, pose estimation, and traditional image/video processing. However, these Multi-Layer Perceptron (MLP) models often grapple with computational and memory challenges. Addressing these, this study introduces an innovative approach using Tensor-Product B-Spline (TPB), offering a promising solution to lessen computational demands without sacrificing accuracy. The central objective was to harness TPB’s potential for image denoising and super-resolution, aiming to sidestep computational burdens of neural fields. This was achieved by replacing iterative processes with deterministic TPB solutions, ensuring enhanced performance and reduced load. The developed framework adeptly manages both super-resolution and denoising, utilizing implicit TPB functions layered to optimize image reconstruction. Evaluation on the Set14 and Kodak datasets showed the TPB-based approach to be comparable to established methods, producing high-quality results in both quantitative metrics and visual evaluations. This pioneering methodology, emphasizing its novelty, offers a refreshed perspective in image processing, setting a promising trajectory for future advancements in the domain.
Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.
We consider hyperspectral phase/amplitude imaging from hyperspectral complex-valued noisy observations. Block-matching and grouping of similar patches are main instruments of the proposed algorithms. The search neighborhood for similar patches spans both the spectral and 2D spatial dimensions. SVD analysis of 3D grouped patches is used for design of adaptive nonlocal bases. Simulation experiments demonstrate high efficiency of developed state-of-the-art algorithms.
The non-stationary nature of image characteristics calls for adaptive processing, based on the local image content. We propose a simple and flexible method to learn local tuning of parameters in adaptive image processing: we extract simple local features from an image and learn the relation between these features and the optimal filtering parameters. Learning is performed by optimizing a user defined cost function (any image quality metric) on a training set. We apply our method to three classical problems (denoising, demosaicing and deblurring) and we show the effectiveness of the learned parameter modulation strategies. We also show that these strategies are consistent with theoretical results from the literature.