HipyrNet: Hypernet-Guided Feature Pyramid network for mixed-exposure correction

TL;DR

HipyrNet introduces a hypernetwork-guided feature pyramid framework that adaptively enhances images with extreme mixed exposures, outperforming existing methods in both qualitative and quantitative evaluations.

Contribution

The paper presents a novel HyperNetwork integrated with a Laplacian Pyramid framework for adaptive mixed-exposure image enhancement, enabling dynamic kernel prediction for improved performance.

Findings

Outperforms existing methods in mixed-exposure correction

Achieves superior qualitative and quantitative results

Sets a new benchmark for adaptive image translation

Abstract

Recent advancements in image translation for enhancing mixed-exposure images have demonstrated the transformative potential of deep learning algorithms. However, addressing extreme exposure variations in images remains a significant challenge due to the inherent complexity and contrast inconsistencies across regions. Current methods often struggle to adapt effectively to these variations, resulting in suboptimal performance. In this work, we propose HipyrNet, a novel approach that integrates a HyperNetwork within a Laplacian Pyramid-based framework to tackle the challenges of mixed-exposure image enhancement. The inclusion of a HyperNetwork allows the model to adapt to these exposure variations. HyperNetworks dynamically generates weights for another network, allowing dynamic changes during deployment. In our model, the HyperNetwork employed is used to predict optimal kernels for Feature Pyramid decomposition, which enables a tailored and adaptive decomposition process for each input image. Our enhanced translational network incorporates multiscale decomposition and reconstruction, leveraging dynamic kernel prediction to capture and manipulate features across varying scales. Extensive experiments demonstrate that HipyrNet outperforms existing methods, particularly in scenarios with extreme exposure variations, achieving superior results in both qualitative and quantitative evaluations. Our approach sets a new benchmark for mixed-exposure image enhancement, paving the way for future research in adaptive image translation.