Wavelet/shearlet hybridized neural networks for biomedical image restoration

Recently, new programming paradigms have emerged that combine parallelism and numerical computations with algorithmic differentiation. This approach allows for the hybridization of neural network techniques for inverse imaging problems with more traditional methods such as wavelet-based sparsity modelling techniques. The benefits are twofold: on the one hand traditional methods with well-known properties can be integrated in neural networks, either as separate layers or tightly integrated in the network, on the other hand, parameters in traditional methods can be trained end-to-end from datasets in a neural network "fashion" (e.g., using Adagrad or Adam optimizers). In this paper, we explore these hybrid neural networks in the context of shearlet-based regularization for the purpose of biomedical image restoration. Due to the reduced number of parameters, this approach seems a promising strategy especially when dealing with small training data sets

Fast Two-step Blind Optical Aberration Correction

The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion. This degradation is characterized by the point-spread function (PSF), which depends on the wavelengths of light and is variable across the imaging field. In this paper, we propose a two-step scheme to correct optical aberrations in a single raw or JPEG image, i.e., without any prior information on the camera or lens. First, we estimate local Gaussian blur kernels for overlapping patches and sharpen them with a non-blind deblurring technique. Based on the measurements of the PSFs of dozens of lenses, these blur kernels are modeled as RGB Gaussians defined by seven parameters. Second, we remove the remaining lateral chromatic aberrations (not contemplated in the first step) with a convolutional neural network, trained to minimize the red/green and blue/green residual images. Experiments on both synthetic and real images show that the combination of these two stages yields a fast state-of-the-art blind optical aberration compensation technique that competes with commercial non-blind algorithms.Comment: 28 pages, 20 figures, accepted at ECCV'22 as a poste

Joint Demosaicking / Rectification of Fisheye Camera Images using Multi-color Graph Laplacian Regulation

To compose one 360 degrees image from multiple viewpoint images taken from different fisheye cameras on a rig for viewing on a head-mounted display (HMD), a conventional processing pipeline first performs demosaicking on each fisheye camera's Bayer-patterned grid, then translates demosaicked pixels from the camera grid to a rectified image grid. By performing two image interpolation steps in sequence, interpolation errors can accumulate, and acquisition noise in each captured pixel can pollute its neighbors, resulting in correlated noise. In this paper, a joint processing framework is proposed that performs demosaicking and grid-to-grid mapping simultaneously, thus limiting noise pollution to one interpolation. Specifically, a reverse mapping function is first obtained from a regular on-grid location in the rectified image to an irregular off-grid location in the camera's Bayer-patterned image. For each pair of adjacent pixels in the rectified grid, its gradient is estimated using the pair's neighboring pixel gradients in three colors in the Bayer-patterned grid. A similarity graph is constructed based on the estimated gradients, and pixels are interpolated in the rectified grid directly via graph Laplacian regularization (GLR). To establish ground truth for objective testing, a large dataset containing pairs of simulated images both in the fisheye camera grid and the rectified image grid is built. Experiments show that the proposed joint demosaicking / rectification method outperforms competing schemes that execute demosaicking and rectification in sequence in both objective and subjective measures

Joint Demosaicking / Rectification of Fisheye Camera Images using Multi-color Graph Laplacian Regulation

To compose one 360 degrees image from multiple viewpoint images taken from different fisheye cameras on a rig for viewing on a head-mounted display (HMD), a conventional processing pipeline first performs demosaicking on each fisheye camera's Bayer-patterned grid, then translates demosaicked pixels from the camera grid to a rectified image grid. By performing two image interpolation steps in sequence, interpolation errors can accumulate, and acquisition noise in each captured pixel can pollute its neighbors, resulting in correlated noise. In this paper, a joint processing framework is proposed that performs demosaicking and grid-to-grid mapping simultaneously, thus limiting noise pollution to one interpolation. Specifically, a reverse mapping function is first obtained from a regular on-grid location in the rectified image to an irregular off-grid location in the camera's Bayer-patterned image. For each pair of adjacent pixels in the rectified grid, its gradient is estimated using the pair's neighboring pixel gradients in three colors in the Bayer-patterned grid. A similarity graph is constructed based on the estimated gradients, and pixels are interpolated in the rectified grid directly via graph Laplacian regularization (GLR). To establish ground truth for objective testing, a large dataset containing pairs of simulated images both in the fisheye camera grid and the rectified image grid is built. Experiments show that the proposed joint demosaicking / rectification method outperforms competing schemes that execute demosaicking and rectification in sequence in both objective and subjective measures