Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction

• URL: http://arxiv.org/abs/2309.01183v2
• Date: Sat, 3 Aug 2024 06:18:23 GMT
• Title: Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
• Authors: Xiaoke Shang, Gehui Li, Zhiying Jiang, Shaomin Zhang, Nai Ding, Jinyuan Liu,
• Abstract summary: The correction of exposure-related issues is a pivotal component in enhancing the quality of images. This paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction.
• Score: 18.014481087171657
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The correction of exposure-related issues is a pivotal component in enhancing the quality of images, offering substantial implications for various computer vision tasks. Historically, most methodologies have predominantly utilized spatial domain recovery, offering limited consideration to the potentialities of the frequency domain. Additionally, there has been a lack of a unified perspective towards low-light enhancement, exposure correction, and multi-exposure fusion, complicating and impeding the optimization of image processing. In response to these challenges, this paper proposes a novel methodology that leverages the frequency domain to improve and unify the handling of exposure correction tasks. Our method introduces Holistic Frequency Attention and Dynamic Frequency Feed-Forward Network, which replace conventional correlation computation in the spatial-domain. They form a foundational building block that facilitates a U-shaped Holistic Dynamic Frequency Transformer as a filter to extract global information and dynamically select important frequency bands for image restoration. Complementing this, we employ a Laplacian pyramid to decompose images into distinct frequency bands, followed by multiple restorers, each tuned to recover specific frequency-band information. The pyramid fusion allows a more detailed and nuanced image restoration process. Ultimately, our structure unifies the three tasks of low-light enhancement, exposure correction, and multi-exposure fusion, enabling comprehensive treatment of all classical exposure errors. Benchmarking on mainstream datasets for these tasks, our proposed method achieves state-of-the-art results, paving the way for more sophisticated and unified solutions in exposure correction.

Related papers

• Unified Image Restoration and Enhancement: Degradation Calibrated Cycle Reconstruction Diffusion Model [8.713784455593778]
  CycleRDM is a novel framework designed to unify restoration and enhancement tasks. It learns the mapping relationships among the degraded domain, the rough normal domain, and the normal domain. To improve restoration quality, we design a feature gain module for the decomposed wavelet high-frequency domain.
  arXiv  Detail & Related papers  (2024-12-19T08:33:33Z)
• Multi-view Image Diffusion via Coordinate Noise and Fourier Attention [5.251293630298169]
  We propose a diffusion process that attends to time-dependent spatial frequencies of features with a novel attention mechanism and cross-attention loss. Our technique improves SOTA on several quantitative metrics with qualitatively better results when compared to other state-of-the-art approaches for multi-view consistency.
  arXiv  Detail & Related papers  (2024-12-04T22:49:40Z)
• Multi-Scale Representation Learning for Image Restoration with State-Space Model [13.622411683295686]
  We propose a novel Multi-Scale State-Space Model-based (MS-Mamba) for efficient image restoration. Our proposed method achieves new state-of-the-art performance while maintaining low computational complexity.
  arXiv  Detail & Related papers  (2024-08-19T16:42:58Z)
• AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation [99.57024606542416]
  We propose an adaptive all-in-one image restoration network based on frequency mining and modulation. Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands. The proposed model achieves adaptive reconstruction by accentuating the informative frequency subbands according to different input degradations.
  arXiv  Detail & Related papers  (2024-03-21T17:58:14Z)
• Misalignment-Robust Frequency Distribution Loss for Image Transformation [51.0462138717502]
  This paper aims to address a common challenge in deep learning-based image transformation methods, such as image enhancement and super-resolution. We introduce a novel and simple Frequency Distribution Loss (FDL) for computing distribution distance within the frequency domain. Our method is empirically proven effective as a training constraint due to the thoughtful utilization of global information in the frequency domain.
  arXiv  Detail & Related papers  (2024-02-28T09:27:41Z)
• A Dual Domain Multi-exposure Image Fusion Network based on the Spatial-Frequency Integration [57.14745782076976]
  Multi-exposure image fusion aims to generate a single high-dynamic image by integrating images with different exposures. We propose a novelty perspective on multi-exposure image fusion via the Spatial-Frequency Integration Framework, named MEF-SFI. Our method achieves visual-appealing fusion results against state-of-the-art multi-exposure image fusion approaches.
  arXiv  Detail & Related papers  (2023-12-17T04:45:15Z)
• Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing [54.168567276280505]
  We propose a novel Mutual Information-driven Triple interaction Network (MITNet) for image dehazing. The first stage, named amplitude-guided haze removal, aims to recover the amplitude spectrum of the hazy images for haze removal. The second stage, named phase-guided structure refined, devotes to learning the transformation and refinement of the phase spectrum.
  arXiv  Detail & Related papers  (2023-08-14T08:23:58Z)
• Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement [75.25451566988565]
  We propose a novel Gated Multi-Resolution Transfer Network (GMTNet) to reconstruct a spatially precise high-quality image from a burst of low-quality raw images. Detailed experimental analysis on five datasets validates our approach and sets a state-of-the-art for burst super-resolution, burst denoising, and low-light burst enhancement.
  arXiv  Detail & Related papers  (2023-04-13T17:54:00Z)
• Focal Frequency Loss for Image Reconstruction and Synthesis [125.7135706352493]
  We show that narrowing gaps in the frequency domain can ameliorate image reconstruction and synthesis quality further. We propose a novel focal frequency loss, which allows a model to adaptively focus on frequency components that are hard to synthesize.
  arXiv  Detail & Related papers  (2020-12-23T17:32:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.