@ignatov2018wespe; @deng2018aesthetic].

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   ![Framework of the proposed network MIRNet that learns enriched feature representations for image restoration and enhancement. MIRNet is based on a recursive residual design. In the core of MIRNet is the multi-scale residual block (MRB) whose main branch is dedicated to maintaining spatially-precise high-resolution representations through the entire network and the complimentary set of parallel branches provide better contextualized features. It also allows information exchange across parallel streams via selective kernel feature fusion (SKFF) in order to consolidate the high-resolution features with the help of low-resolution features, and vice versa.[]{data-label="fig:framework"}](Images/framework.png "fig:"){width="\textwidth"}
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Proposed Method
===============

In this section, we first present an overview of the proposed MIRNet for image restoration and enhancement, illustrated in Fig. \[fig:framework\]. We then provide details of the *multi-scale residual block*, which is the fundamental building block of our method, containing several key elements: **(a)** parallel multi-resolution convolution streams for extracting (fine-to-coarse) semantically-richer and (coarse-to-fine) spatially-precise feature representations, **(b)** information exchange across multi-resolution streams, **(c)** attention-based aggregation of features arriving from multiple streams, **(d)** dual-attention units to capture contextual information in both spatial and channel dimensions, and **(e)** residual resizing modules to perform downsampling and upsampling operations.

**Overall Pipeline.** Given an image $\mathbf{I} 