Rain Removal

MPRNet is a multi-stage progressive image restoration architecture that progressively learns restoration functions for the degraded inputs, thereby breaking down the overall recovery process into more manageable steps. Specifically, the model first learns the contextualized features using encoder-decoder architectures and later combines them with a high-resolution branch that retains local information. At each stage, a per-pixel adaptive design is introduced that leverages in-situ supervised attention to reweight the local features.

BIDeN, or Blind Image Decomposition Network, is a model for blind image decomposition, which requires separating a superimposed image into constituent underlying images in a blind setting, that is, both the source components involved in mixing as well as the mixing mechanism are unknown. For example, rain may consist of multiple components, such as rain streaks, raindrops, snow, and haze. The Figure shows an example where $N = 4, L = 2, x = {a, b, c, d}$, and $I = {1, 3}$. $a, c$ are selected then passed to the mixing function $f$, and outputs the mixed input image $z$, which is $f\left(a, c\right)$ here. The generator consists of an encoder $E$ with three branches and multiple heads $H$. $\bigotimes$ denotes the concatenation operation. Depth and receptive field of each branch is different to capture multiple scales of features. Each specified head points to the corresponding source component, and the number of heads varies with the maximum number of source components N. All reconstructed images $\left(a', c'\right)$ and their corresponding real images $\left(a, c\right)$ are sent to an unconditional discriminator. The discriminator also predicts the source components of the input image $z$. The outputs from other heads $\left(b', d'\right)$ do not contribute to the optimization.