In recent years, modern imaging sensors and systems have become increasingly complex following the growing demand for high-quality and high-resolution imaging. Commercially available sensors having 30-40 mega-pixel resolutions are common nowadays, while professional and scientific imaging systems (e.g. telescope and satellite sensors) often have hundreds of mega-pixels or even giga-pixel resolution. Such sensors or multi-sensor systems generate extreme amounts of data and require an excessive amount of power in order to process and stream-out the acquired data at the required speed. Consequently, three difficult and closely related bottlenecks appear in such systems: power consumption, data bandwidth and memory storage. Nevertheless, the vast majority of applications (such as machine vision, bio-medical, surveillance, space imaging, etc.) do not process all the data acquired from the conventional image acquisition. In fact, most of the image (video) data is extremely redundant in both temporal and spatial domain. Moreover, in many of these applications, this excessive data is simply discarded despite the additional resources that were used to acquire it. This Thesis explores new and and "smart" ways of image acquisition that can exploit this redundancy and reduce the required image data. The ultimate goal of the research conducts into reducing the power consumption, system bandwidth, memory requirements and improve the overall system efficiency in numerous modern image acquisition applications. The main focus of this Thesis is the investigation of three unconventional approaches to image acquisition that offer power, bandwidth and memory storage reduction. All three methods are explored in the context of CMOS integrated circuit implementation directly on the focal-plane in order to maximize their efficiency. The first investigated method is called "relative imaging" (RI). The image sensor detects the relative ratios of the photo-currents corresponding to the neighboring pixels without performing pixel integration. This method allows high frame-rates with a very high dynamic range. Moreover, detecting the photo-current ratios (instead of the absolute amplitudes) corresponds better to the intrinsic nature of VLSI design. The second investigated method which enables the reduction of the required image information is compressive sampling (CS). Compressive sampling exploits image sparsity in order to reduce the amount of information that is required to guarantee correct image reconstruction. The third method, multi-band edge detection (MBED), decomposes the image content into separate frequency bands and performs single-bit quantization of each frequency band separately. It achieves 1-1.33 bit-per-pixel compression level, while preserving most of the image information and demonstrating robust performance in the presence of noise and other physical non-idealities. An additional focus of this Thesis is the design of unconventional analog-to-digital converter (ADC) topologies convenient for "smart" image acquisition.