Efficient training procedures for multi-spectral demosaicing

The simultaneous acquisition of multi-spectral images on a single sensor can be efficiently performed by single shot capture using a mutli-spectral filter array. This paper focused on the demosaicing of color and near-infrared bands and relied on a convolutional neural network (CNN). To train the deep learning model robustly and accurately, it is necessary to provide enough training data, with sufficient variability. We focused on the design of an efficient training procedure by discovering an optimal training dataset. We propose two data selection strategies, motivated by slightly different concepts. The general term that will be used for the proposed models trained using data selection is data selection-based multi-spectral demosaicing (DSMD). The first idea is clustering-based data selection (DSMD-C), with the goal to discover a representative subset with a high variance so as to train a robust model. The second is an adaptive-based data selection (DSMD-A), a self-guided approach that selects new data based on the current model accuracy. We performed a controlled experimental evaluation of the proposed training strategies and the results show that a careful selection of data does benefit the speed and accuracy of training. We are still able to achieve high reconstruction accuracy with a lightweight model

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