Increasing imaging resolution by covering your sensor

Abstract

Up to now, an increase in camera resolution required image sensors with more and more pixels. However, acquisition systems are limited in their pixels per second throughput given as power and complexity constraints. Simply capturing more pixels in a given system is often not possible. We propose a new non-regular imaging architecture that samples only few pixels and reconstructs a high resolution image afterwards. Our sampling is optimized to provide non-regular spatial sampling from a sensor with regular readout circuits. An existing slow image acquisition system can then be used to capture the data. The image reconstruction is performed with a local sparsity-based approach. The result is a high resolution image that requires a much smaller effort during acquisition

High dynamic range video by spatially non-regular optical filtering

“…The FSE reconstruction is operated with the same parameters as in [10]. In our simulated images we discard under-and overexposed pixels by thresholding.…”

“…Similar to [10] we assume that natural images are non-stationary and a somewhat evenly distributed sampling is better for adjusting to changes in statistical properties of the image. In the design of sampling patterns we make sure that the pattern is balanced in regions of 4×4.…”

“…However, we expect further improvement in reconstruction quality by giving higher weight to pixels with better Signalto-Noise Ratio (SNR). The weight is further refined to w(m, n) with an exponential decay as in traditional FSE [10]. During the reconstruction ambiguities may arise during selection of basis functions.…”

“…Similar to the non-regular sampling problem described in [10] we perform an iterative and overlapping block based reconstruction. We iteratively generate the sparse model for a block with local coordinates (m, n)…”

“…The improvement comes from a non-regular arrangement of attenuation filters and subsequent reconstruction. As we have shown in [10] a non-regular sampling pattern will not introduce aliasing and a sparsity-based reconstruction can be used effectively to recover missing pixels at high resolution. The paper is organized as follows: At first, we describe a model for image acquisition and non-regular sampling in Section 2 and 3.…”