De-velopment of Demosaicking Techniques for Multi-Spectral Imaging Using Mosaic Focal Plane Arrays

The use of mosaicked array technology in commercial digital cameras has madethem smaller, cheaper and mechanically more robust. In a mosaicked sensor, each pixel detector is covered with a wavelength-specific optical filter. Since only one spectral band is sensed per pixel location, there is an absence of information from the rest of the spectral bands. These unmeasured spectral bands are estimated by using information obtained from the neighborhood pixels. This process of estimating the unmeasured spectral band information is called demosaicking. The demosaicking process uses interpolation strategies to estimate the missing pixels. Sophisticated interpolation methods have been developed for performing this task in digital color cameras.In this thesis we propose to evaluate the adaptation of the mosaicked technol- ogy for multi-spectral cameras. Existing multi-spectral cameras use traditional methods like imaging spectrometers to capture a multi-spectral image. These methods are very expensive and delicate in nature. The objective of using the mosaicked technology for multi-spectral cameras is to reap the same benefits it offers in the commercial digital color cameras. However, the problem in using the mosaicked technology for multi-spectral images is the huge amount of missing pixels that need to be estimated in order to form the multi-spectral image. The estimation process becomes even more complicated as the number of bands in the multi-spectral image increases. Traditional demosaicking algorithms cannot be used because they have been specifically designed to suit three-band color images.This thesis focuses on developing new demosaicking algorithms for multi- spectral images. The existing demosaicking algorithms for color images have been extended for multi-spectral images. A new variation of the bilinear interpolationbased strategy has been developed to perform demosaicking. This demosaicking method uses variable neighborhood definitions to interpolate the missing spectral band values at each pixel locations in a multi-spectral image. A novel Maximum a-Posteriori (MAP) based demosaicking method has also been developed. This method treats demosaicking as an image restoration problem. It can derive op- timal estimation result that resembles the original image the best. In addition, it can simultaneously perform interpolation of missing spectral bands at pixel locations and also remove noise and degradations in the image.Extensive experimentation and comparisons have shown that the new demo- saicking methods for multi-spectral images developed in this thesis perform better than the traditional interpolation trategies. The outputs from the demosaicking methods have been shown to be better reconstructed estimates of the original im- ages and also have the ability to produce good classification results in applicationslike target recognition and discrimination

Inheriting Bayer's Legacy-Joint Remosaicing and Denoising for Quad Bayer Image Sensor

Pixel binning based Quad sensors have emerged as a promising solution to overcome the hardware limitations of compact cameras in low-light imaging. However, binning results in lower spatial resolution and non-Bayer CFA artifacts. To address these challenges, we propose a dual-head joint remosaicing and denoising network (DJRD), which enables the conversion of noisy Quad Bayer and standard noise-free Bayer pattern without any resolution loss. DJRD includes a newly designed Quad Bayer remosaicing (QB-Re) block, integrated denoising modules based on Swin-transformer and multi-scale wavelet transform. The QB-Re block constructs the convolution kernel based on the CFA pattern to achieve a periodic color distribution in the perceptual field, which is used to extract exact spectral information and reduce color misalignment. The integrated Swin-Transformer and multi-scale wavelet transform capture non-local dependencies, frequency and location information to effectively reduce practical noise. By identifying challenging patches utilizing Moire and zipper detection metrics, we enable our model to concentrate on difficult patches during the post-training phase, which enhances the model's performance in hard cases. Our proposed model outperforms competing models by approximately 3dB, without additional complexity in hardware or software

Computationally efficient locally adaptive demosaicing of color filter array images using the dual-tree complex wavelet packet transform

Most digital cameras use an array of alternating color filters to capture the varied colors in a scene with a single sensor chip. Reconstruction of a full color image from such a color mosaic is what constitutes demosaicing. In this paper, a technique is proposed that performs this demosaicing in a way that incurs a very low computational cost. This is done through a (dual-tree complex) wavelet interpretation of the demosaicing problem. By using a novel locally adaptive approach for demosaicing (complex) wavelet coefficients, we show that many of the common demosaicing artifacts can be avoided in an efficient way. Results demonstrate that the proposed method is competitive with respect to the current state of the art, but incurs a lower computational cost. The wavelet approach also allows for computationally effective denoising or deblurring approaches

High performance image demosaicing hardware designs

Most digital cameras capture only one color channel (red, green, or blue) per pixel, because capturing three color channels per pixel would require three image sensors which increases the cost of digital cameras. Therefore, only one image sensor is used, and images pass through a color filter array (CFA) before being captured by the image sensor. Bayer pattern is the most commonly used CFA pattern in digital cameras. Demosaicing is the process of reconstructing the missing color channels of the pixels in the color filtered image using their available neighboring pixels. There are many image demosaicing algorithms with varying reconstructed image quality and computational complexity. In this thesis, high performance hardware architectures are designed for three high quality image demosaicing algorithms, and the proposed hardware architectures are implemented on FPGA. A high performance hardware architecture for Effective Color Interpolation (ECI) demosaicing algorithm is proposed. A modified version of Enhanced ECI demosaicing algorithm and a high performance hardware architecture for this image demosaicing algorithm are proposed. A hybrid ECI and Alternating Projections demosaicing algorithm and a high performance hardware architecture for this image demosaicing algorithm are proposed. The proposed hardware architectures are implemented using Verilog HDL. The Verilog RTL codes are mapped to Xilinx Virtex 6 FPGA. The proposed FPGA implementations are verified with post place & route simulations. They are capable of processing 160, 118, and 119 full HD images per second

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

Informative sensing : theory and applications

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2012.Cataloged from PDF version of thesis.Includes bibliographical references (p. 145-156).Compressed sensing is a recent theory for the sampling and reconstruction of sparse signals. Sparse signals only occupy a tiny fraction of the entire signal space and thus have a small amount of information, relative to their dimension. The theory tells us that the information can be captured faithfully with few random measurement samples, even far below the Nyquist rate. Despite the successful story, we question how the theory would change if we had a more precise prior than the simple sparsity model. Hence, we consider the settings where the prior is encoded as a probability density. In a Bayesian perspective, we see the signal recovery as an inference, in which we estimate the unmeasured dimensions of the signal given the incomplete measurements. We claim that good sensors should somehow be designed to minimize the uncertainty of the inference. In this thesis, we primarily use Shannon's entropy to measure the uncertainty and in effect pursue the InfoMax principle, rather than the restricted isometry property, in optimizing the sensors. By approximate analysis on sparse signals, we found random projections, typical in the compressed sensing literature, to be InfoMax optimal if the sparse coefficients are independent and identically distributed (i.i.d.). If not, however, we could find a different set of projections which, in signal reconstruction, consistently outperformed random or other types of measurements. In particular, if the coefficients are groupwise i.i.d., groupwise random projections with nonuniform sampling rate per group prove asymptotically Info- Max optimal. Such a groupwise i.i.d. pattern roughly appears in natural images when the wavelet basis is partitioned into groups according to the scale. Consequently, we applied the groupwise random projections to the sensing of natural images. We also considered designing an optimal color filter array for single-chip cameras. In this case, the feasible set of projections is highly restricted because multiplexing across pixels is not allowed. Nevertheless, our principle still applies. By minimizing the uncertainty of the unmeasured colors given the measured ones, we could find new color filter arrays which showed better demosaicking performance in comparison with Bayer or other existing color filter arrays.by Hyun Sung Chang.Ph.D

Low-complexity color demosaicing algorithm based on integrated gradients

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