Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement

• URL: http://arxiv.org/abs/2407.13170v1
• Date: Thu, 18 Jul 2024 05:18:43 GMT
• Title: Unified-EGformer: Exposure Guided Lightweight Transformer for Mixed-Exposure Image Enhancement
• Abstract summary: We introduce the Unified-Exposure Guided Transformer (Unified-EGformer) Our proposed solution is built upon advanced transformer architectures, equipped with local pixel-level refinement and global refinement blocks for color correction and image-wide adjustments. U-EGformer, with a lightweight design featuring a memory footprint (peak memory) of only $sim$1134 MB (0.1 Million parameters) and an inference time of 95 ms (9.61x faster than the average) is a viable choice for real-time applications such as surveillance and autonomous navigation.
• Score: 11.328110218032027
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Despite recent strides made by AI in image processing, the issue of mixed exposure, pivotal in many real-world scenarios like surveillance and photography, remains inadequately addressed. Traditional image enhancement techniques and current transformer models are limited with primary focus on either overexposure or underexposure. To bridge this gap, we introduce the Unified-Exposure Guided Transformer (Unified-EGformer). Our proposed solution is built upon advanced transformer architectures, equipped with local pixel-level refinement and global refinement blocks for color correction and image-wide adjustments. We employ a guided attention mechanism to precisely identify exposure-compromised regions, ensuring its adaptability across various real-world conditions. U-EGformer, with a lightweight design featuring a memory footprint (peak memory) of only $\sim$1134 MB (0.1 Million parameters) and an inference time of 95 ms (9.61x faster than the average), is a viable choice for real-time applications such as surveillance and autonomous navigation. Additionally, our model is highly generalizable, requiring minimal fine-tuning to handle multiple tasks and datasets with a single architecture.

Related papers

• There and Back Again: A Flexible-Frame Transformer for Multi-Exposure Fusion [33.80456021651473]
  We propose FreeMEF, a flexible-frame transformer for multi-exposure fusion.<n>First, we introduce a recurrent state space module that sequentially fuses features from arbitrary sequences.<n>Second, we devise a global feature guided block (GFGB) incorporating an extremity-aware hybrid attention (EAHA) and an affine-injection feed-forward network (AFFN)
  arXiv  Detail & Related papers  (2026-06-26T09:54:00Z)
• VeraRetouch: A Lightweight Fully Differentiable Framework for Multi-Task Reasoning Photo Retouching [27.18046653314556]
  VeraRetouch is a lightweight and fully differentiable framework for multi-task photo retouching.<n>We employ a 0.5B Vision-Language Model (VLM) as the central intelligence to formulate retouching plans.<n>We develop a fully differentiable Retouch Renderer that replaces external tools.
  arXiv  Detail & Related papers  (2026-04-30T03:39:32Z)
• DST-Calib: A Dual-Path, Self-Supervised, Target-Free LiDAR-Camera Extrinsic Calibration Network [57.22935789233992]
  This article presents the first self-supervised LiDAR-camera extrinsic calibration network that operates in an online fashion.<n>The proposed method significantly outperforms existing approaches in terms of generalizability.
  arXiv  Detail & Related papers  (2026-01-03T13:57:01Z)
• IntrinsicEdit: Precise generative image manipulation in intrinsic space [53.404235331886255]
  We introduce a versatile, generative workflow that operates in an intrinsic-image latent space.<n>We address key challenges of identity preservation and intrinsic-channel entanglement.<n>We enable precise, efficient editing with automatic resolution of global illumination effects.
  arXiv  Detail & Related papers  (2025-05-13T18:24:15Z)
• Feature Alignment with Equivariant Convolutions for Burst Image Super-Resolution [52.55429225242423]
  We propose a novel framework for Burst Image Super-Resolution (BISR), featuring an equivariant convolution-based alignment. This enables the alignment transformation to be learned via explicit supervision in the image domain and easily applied in the feature domain. Experiments on BISR benchmarks show the superior performance of our approach in both quantitative metrics and visual quality.
  arXiv  Detail & Related papers  (2025-03-11T11:13:10Z)
• OminiControl: Minimal and Universal Control for Diffusion Transformer [68.3243031301164]
  OminiControl is a framework that integrates image conditions into pre-trained Diffusion Transformer (DiT) models. At its core, OminiControl leverages a parameter reuse mechanism, enabling the DiT to encode image conditions using itself as a powerful backbone. OminiControl addresses a wide range of image conditioning tasks in a unified manner, including subject-driven generation and spatially-aligned conditions.
  arXiv  Detail & Related papers  (2024-11-22T17:55:15Z)
• WTCL-Dehaze: Rethinking Real-world Image Dehazing via Wavelet Transform and Contrastive Learning [17.129068060454255]
  Single image dehazing is essential for applications such as autonomous driving and surveillance. We propose an enhanced semi-supervised dehazing network that integrates Contrastive Loss and Discrete Wavelet Transform. Our proposed algorithm achieves superior performance and improved robustness compared to state-of-the-art single image dehazing methods.
  arXiv  Detail & Related papers  (2024-10-07T05:36:11Z)
• Towards Effective User Attribution for Latent Diffusion Models via Watermark-Informed Blending [54.26862913139299]
  We introduce a novel framework Towards Effective user Attribution for latent diffusion models via Watermark-Informed Blending (TEAWIB) TEAWIB incorporates a unique ready-to-use configuration approach that allows seamless integration of user-specific watermarks into generative models. Experiments validate the effectiveness of TEAWIB, showcasing the state-of-the-art performance in perceptual quality and attribution accuracy.
  arXiv  Detail & Related papers  (2024-09-17T07:52:09Z)
• Deep 3D World Models for Multi-Image Super-Resolution Beyond Optical Flow [27.31768206943397]
  Multi-image super-resolution (MISR) allows to increase the spatial resolution of a low-resolution (LR) acquisition by combining multiple images. Our proposed model, called EpiMISR, moves away from optical flow and explicitly uses the epipolar geometry of the acquisition process.
  arXiv  Detail & Related papers  (2024-01-30T12:55:49Z)
• A Non-Uniform Low-Light Image Enhancement Method with Multi-Scale Attention Transformer and Luminance Consistency Loss [11.585269110131659]
  Low-light image enhancement aims to improve the perception of images collected in dim environments. Existing methods cannot adaptively extract the differentiated luminance information, which will easily cause over-exposure and under-exposure. We propose a multi-scale attention Transformer named MSATr, which sufficiently extracts local and global features for light balance to improve the visual quality.
  arXiv  Detail & Related papers  (2023-12-27T10:07:11Z)
• Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming [0.728258471592763]
  Enhancing low-light images while maintaining natural colors is a challenging problem due to camera processing variations. We propose Dimma, a semi-supervised approach that aligns with any camera by utilizing a small set of image pairs. We achieve that by introducing a convolutional mixture density network that generates distorted colors of the scene based on the illumination differences.
  arXiv  Detail & Related papers  (2023-10-14T17:59:46Z)
• Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction [65.5397271106534]
  A single neural network is difficult to handle all exposure problems. In particular, convolutions hinder the ability to restore faithful color or details on extremely over-/under- exposed regions. We propose a Macro-Micro-Hierarchical transformer, which consists of a macro attention to capture long-range dependencies, a micro attention to extract local features, and a hierarchical structure for coarse-to-fine correction.
  arXiv  Detail & Related papers  (2023-09-02T09:07:36Z)
• Low-Light Image Enhancement with Illumination-Aware Gamma Correction and Complete Image Modelling Network [69.96295927854042]
  Low-light environments usually lead to less informative large-scale dark areas. We propose to integrate the effectiveness of gamma correction with the strong modelling capacities of deep networks. Because exponential operation introduces high computational complexity, we propose to use Taylor Series to approximate gamma correction.
  arXiv  Detail & Related papers  (2023-08-16T08:46:51Z)
• Searching a Compact Architecture for Robust Multi-Exposure Image Fusion [55.37210629454589]
  Two major stumbling blocks hinder the development, including pixel misalignment and inefficient inference. This study introduces an architecture search-based paradigm incorporating self-alignment and detail repletion modules for robust multi-exposure image fusion. The proposed method outperforms various competitive schemes, achieving a noteworthy 3.19% improvement in PSNR for general scenarios and an impressive 23.5% enhancement in misaligned scenarios.
  arXiv  Detail & Related papers  (2023-05-20T17:01:52Z)
• Variational Approach for Intensity Domain Multi-exposure Image Fusion [11.678822620192435]
  We present a method to produce well-exposed fused image that can be displayed directly on conventional display devices. The ambition is to preserve details in poorly illuminated and brightly illuminated regions.
  arXiv  Detail & Related papers  (2022-07-09T06:31:34Z)
• Plug-In Inversion: Model-Agnostic Inversion for Vision with Data Augmentations [61.95114821573875]
  We introduce Plug-In Inversion, which relies on a simple set of augmentations and does not require excessive hyper- parameter tuning. We illustrate the practicality of our approach by inverting Vision Transformers (ViTs) and Multi-Layer Perceptrons (MLPs) trained on the ImageNet dataset.
  arXiv  Detail & Related papers  (2022-01-31T02:12:45Z)

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.