Cameras often deviate from ideal image formation because of optical, sensor, and scene-dependent physics. These deviations are usually treated as errors to suppress, but they can also provide useful structure when modeled during reconstruction. This dissertation develops a unified measurement-model perspective for computational imaging: a physical effect can be treated either as an encoding to invert or as a nuisance to suppress, depending on the scene properties the system aims to recover. Across spectral and thermal imaging, this perspective enables practical cameras to recover richer scene information from measurements that would otherwise appear noisy, ambiguous, or inconsistent.

First, we study hyperspectral imaging, where light must be distributed across many wavelength channels. This creates a photon-efficiency trade-off among spectral resolution, spatial resolution, acquisition time, and noise. Two systems address this trade-off in complementary ways. HyperColorization uses a dense grayscale guide image to propagate sparse spectral measurements, exploiting the high photon efficiency of broadband grayscale imaging while recovering dense spectra. Spectrum from Defocus instead uses longitudinal chromatic aberration as an optical encoding: a focal stack captured with simple refractive optics and a grayscale sensor provides wavelength-dependent blur that can be calibrated and inverted. Together, these systems show how scene structure and optical structure can reduce the cost of spectral imaging without relying on complex spectral hardware.

Later we study thermal imaging for novel view synthesis. Thermal cameras operate in darkness and adverse visibility, but their measurements are not stable temperature images: they contain drift, fixed-pattern noise, weak texture, limited dynamic range, and view-dependent reflection. The first thermal system addresses the sensor-driven part of this problem by characterizing these degradations across public multiview datasets, stabilizing thermal video before reconstruction, and absorbing residual radiometric variation during Gaussian splatting. The second thermal system treats view-dependent thermal appearance not only as an error source but also as a cue. By exploiting the fact that emitted radiance is approximately view-stable while reflected radiance changes with viewpoint, it decomposes reconstructed thermal scenes into temperature-correlated emission and material-dependent reflection.

Across these systems, the same principle recurs: the role of a physical effect is determined by the reconstruction target. Chromatic aberration can be suppressed in a conventional RGB camera or inverted as a spectral measurement; thermal reflection can be absorbed as nuisance variation or estimated as scene content. By designing sensing models and reconstruction algorithms together, this dissertation shows that practical cameras can turn structured deviations from ideal imaging into physically meaningful measurements.