# A Deep Optimization Approach for Image Deconvolution

## TL;DR

This paper introduces the Golf Optimizer, a lightweight deep learning network that learns priors for blind image deconvolution, improving stability and performance over traditional methods.

## Contribution

The paper presents a novel Golf Optimizer network that effectively learns deep priors with better propagation, enhancing blind image deconvolution results.

## Key findings

- Achieves competitive performance on GoPro dataset.
- Extremely lightweight compared to state-of-the-art methods.
- Improves stability of priors in deconvolution.

## Abstract

In blind image deconvolution, priors are often leveraged to constrain the solution space, so as to alleviate the under-determinacy. Priors which are trained separately from the task of deconvolution tend to be instable, or ineffective. We propose the Golf Optimizer, a novel but simple form of network that learns deep priors from data with better propagation behavior. Like playing golf, our method first estimates an aggressive propagation towards optimum using one network, and recurrently applies a residual CNN to learn the gradient of prior for delicate correction on restoration. Experiments show that our network achieves competitive performance on GoPro dataset, and our model is extremely lightweight compared with the state-of-art works.