Computational imaging and sensing leverages the joint-design of optics, detectors and processing to overcome the performance bottlenecks inherent to the traditional imaging paradigm. This novel imaging and sensing design paradigm essentially allows new trade-offs between the optics, detector and processing components of an imaging system and enables broader operational regimes beyond the reach of conventional imaging architectures, which are constrained by well-known Rayleigh, Strehl and Nyquist rules amongst others. In this dissertation, we focus on scalability aspects of these novel computational imaging architectures, their design and implementation, which have far-reaching impacts on the potential and feasibility of realizing task-specific performance gains relative to traditional imager designs. For the extended depth of field (EDoF) computational imager design, which employs a customized phase mask to achieve defocus immunity, we propose a joint-optimization framework to simultaneously optimize the parameters of the optical phase mask and the processing algorithm, with the system design goal of minimizing the noise and artifacts in the final processed image. Using an experimental prototype, we demonstrate that our optimized system design achieves higher fidelity output compared to other static designs from the literature, such as the Cubic and Trefoil phase masks. While traditional imagers rely on an isomorphic mapping between the scene and the optical measurements to form images, they do not exploit the inherent compressibility of natural images and thus are subject to Nyquist sampling. Compressive sensing exploits the inherent redundancy of natural images, basis of image compression algorithms like JPEG/JPEG2000, to make linear projection measurements with far fewer samples than Nyquist for the image forming task. Here, we present a block wise compressive imaging architecture which is scalable to high space-bandwidth products (i.e. large FOV and high resolution applications) and employs a parallelizable and non-iterative piecewise linear reconstruction algorithm capable of operating in real-time. Our compressive imager based on this scalable architecture design is not limited to the imaging task and can also be used for automatic target recognition (ATR) without an intermediate image reconstruction. To maximize the detection and classification performance of this compressive ATR sensor, we have developed a scalable statistical model of natural scenes, which enables the optimization of the compressive sensor projections with the Cauchy-Schwarz mutual information metric. We demonstrate the superior performance of this compressive ATR system using simulation and experiment. Finally, we investigate the fundamental resolution limit of imaging via the canonical incoherent quasi-monochromatic two point-sources separation problem. We extend recent results in the literature demonstrating, with Fisher information and estimator mean square error analysis, that a passive optical mode-sorting architecture with only two measurements can outperform traditional intensity-based imagers employing an ideal focal plane array in the sub-Rayleigh range, thus overcoming the Rayleigh resolution limit.