**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.

**GAGNN**, or **Group-aware Graph Neural Network**, is a hierarchical model for nationwide city air quality forecasting. The model constructs a city graph and a city group graph to model the spatial and latent dependencies between cities, respectively. GAGNN introduces differentiable grouping network to discover the latent dependencies among cities and generate city groups. Based on the generated city groups, a group correlation encoding module is introduced to learn the correlations between them, which can effectively capture the dependencies between city groups. After the graph construction, GAGNN implements message passing mechanism to model the dependencies between cities and city groups.

GAGNN

Group-Aware Graph Neural Network for Nationwide City Air Quality Forecasting

DeepIR

Thermal Image Processing via Physics-Inspired Deep Networks

**Decaying Momentum**, or **Demon**, is a stochastic optimizer motivated by decaying the total contribution of a gradient to all future updates. By decaying the momentum parameter, the total contribution of a gradient to all future updates is decayed. A particular gradient term $g\_{t}$ contributes a total of  $\eta\sum\_{i}\beta^{i}$ of its "energy" to all future gradient updates, and this results in the geometric sum, $\sum^{\infty}\_{i=1}\beta^{i} = \beta\sum^{\infty}\_{i=0}\beta^{i} = \frac{\beta}{\left(1-\beta\right)}$. Decaying this sum results in the Demon algorithm. Letting $\beta\_{init}$ be the initial $\beta$; then at the current step $t$ with total $T$ steps, the decay routine is given by solving the below for $\beta\_{t}$:

$$ \frac{\beta\_{t}}{\left(1-\beta\_{t}\right)} =  \left(1-t/T\right)\beta\_{init}/\left(1-\beta\_{init}\right)$$

Where $\left(1-t/T\right)$ refers to the proportion of iterations remaining. Note that Demon typically requires no hyperparameter tuning as it is usually decayed to $0$ or a small negative value at time 
$T$. Improved performance is observed by delaying the decaying. Demon can be applied to any gradient descent algorithm with a momentum parameter.

Source	Thermal Image Processing via Physics-Inspired Deep Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com