Thermal Image Processing Models

DeepIR

Introduced by Saragadam et al. in Thermal Image Processing via Physics-Inspired Deep Networks

DeepIR, or Deep InfraRed image processing, is a thermal image processing framework for recovering high quality images from a very small set of images captured with camera motion. Enhancement is achieved by noting that camera motion, which is usually a hinderance, can be exploited to our advantage to separate a sequence of images into the scene-dependent radiant flux, and a slowly changing scene-independent non-uniformity. DeepIR combines the physics of microbolometer sensors, with powerful regularization capabilities by neural network-based representations. DeepIR relies on the key observation that jittering a camera, while unwanted in visible domain, is highly desirable in the thermal domain as it allows an accurate separation of the sensor-specific non-uniformities from the scene’s radiant flux.

Source: Thermal Image Processing via Physics-Inspired Deep Networks

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Denoising 1 33.33%
Sensor Modeling 1 33.33%
Super-Resolution 1 33.33%

Components


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🤖 No Components Found You can add them if they exist; e.g. Mask R-CNN uses RoIAlign

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