On-chip spectral filter arrays enable compact, snapshot hyperspectral cameras that could be useful for applications ranging from agriculture to medical imaging and robotics. The spectral range and resolution of these cameras are determined by the number of spectral filters on the sensor, which are usually grouped together in repeating super-pixels, similar to the structure of a Bayer filter. Many tasks require dozens to hundreds of spectral channels, but this usually comes at the cost of a proportional decrease in spatial resolution and light throughput. We propose a multi-shot approach that leverages lens defocus to encode light before it is filtered by an on-chip spectral filter. Using this approach, we capture a burst of spatio-spectral encoded measurements at different focus planes, then solve an inverse problem to recover a high-resolution spatio-spectral datacube. Since most lens-based cameras already have a focusing mechanism, our approach is practical and could be broadly used by spectral filter array-based hyperspectral cameras. We provide analysis of our proposed system and comparisons against alternative methods. Finally, we demonstrate a prototype system and present experimental results of challenging dense, daylight scenes, demonstrating greater than $3 \times$ sub-super-pixel super-resolution.

High-dynamic-range (HDR) imaging is an essential technique for overcoming the dynamic range limits of image sensors. The classic method relies on multiple exposures, which slows capture time, resulting in motion artifacts when imaging dynamic scenes. Single-shot HDR imaging alleviates this issue by encoding HDR data in a single exposure, then computationally recovering it. Many established methods use strong image priors to recover improperly exposed detail; these approaches struggle with extended highlight regions. In this work, we demonstrate a novel single-shot HDR capture method that utilizes the global reset release (GRR) shutter mode commonly found in off-the-shelf sensors. GRR shutter mode applies a longer exposure time to rows closer to the bottom of the sensor. We use optics that relay a randomly permuted (shuffled) image onto the sensor, effectively creating spatially randomized exposures across the scene. The resulting exposure diversity allows us to recover HDR data by solving an optimization problem with a simple total variation image prior. In simulation, we demonstrate that our method outperforms other single-shot methods when many sensor pixels are saturated (10% or more), and is competitive at modest saturation (1%). Finally, we demonstrate a physical lab prototype that uses an off-the-shelf random fiber bundle for the optical shuffling. The fiber bundle is coupled to a low-cost commercial sensor operating in GRR shutter mode. Our prototype achieves a dynamic range of up to 73dB using an 8-bit sensor with 48dB dynamic range.