Commonly, image processing is handled on a CPU that is connected to the image sensor by a wire. In these far-sensor processing architectures, there is energy loss associated with sending data across an interconnect from the sensor to the CPU. In an effort to increase energy efficiency, near-sensor processing architectures have been developed, in which the sensor and processor are stacked directly on top of each other. This reduces energy loss associated with sending data off-sensor. However, processing near the image sensor causes the sensor to heat up. Reports of thermal noise in near-sensor processing architectures motivated us to study how temperature affects image quality on a commercial image sensor and how thermal noise affects computer vision task accuracy. We analyzed image noise across nine different temperatures and three sensor configurations to determine how image noise responds to an increase in temperature. Ultimately, our team used this information, along with transient analysis of a stacked image sensor’s thermal behavior, to advise thermal management strategies that leverage the benefits of near-sensor processing and prevent accuracy loss at problematic temperatures.