Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising

• URL: http://arxiv.org/abs/2104.01073v1
• Date: Fri, 2 Apr 2021 14:37:20 GMT
• Title: Enhancing Underwater Image via Adaptive Color and Contrast Enhancement, and Denoising
• Authors: Xinjie Li, Guojia Hou, Kunqian Li
• Abstract summary: We propose an adaptive color and contrast enhancement, and denoising (ACCE-D) framework for underwater image enhancement. We derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure. Experimental results demonstrate that our strategy is effective in color correction, visibility improvement, and detail revealing.
• Score: 2.298932494750101
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Images captured underwater are often characterized by low contrast, color distortion, and noise. To address these visual degradations, we propose a novel scheme by constructing an adaptive color and contrast enhancement, and denoising (ACCE-D) framework for underwater image enhancement. In the proposed framework, Gaussian filter and Bilateral filter are respectively employed to decompose the high-frequency and low-frequency components. Benefited from this separation, we utilize soft-thresholding operation to suppress the noise in the high-frequency component. Accordingly, the low-frequency component is enhanced by using an adaptive color and contrast enhancement (ACCE) strategy. The proposed ACCE is a new adaptive variational framework implemented in the HSI color space, in which we design a Gaussian weight function and a Heaviside function to adaptively adjust the role of data item and regularized item. Moreover, we derive a numerical solution for ACCE, and adopt a pyramid-based strategy to accelerate the solving procedure. Experimental results demonstrate that our strategy is effective in color correction, visibility improvement, and detail revealing. Comparison with state-of-the-art techniques also validate the superiority of propose method. Furthermore, we have verified the utility of our proposed ACCE-D for enhancing other types of degraded scenes, including foggy scene, sandstorm scene and low-light scene.

Related papers

• Unveiling Advanced Frequency Disentanglement Paradigm for Low-Light Image Enhancement [61.22119364400268]
  We propose a novel low-frequency consistency method, facilitating improved frequency disentanglement optimization. Noteworthy improvements are showcased across five popular benchmarks, with up to 7.68dB gains on PSNR achieved for six state-of-the-art models. Our approach maintains efficiency with only 88K extra parameters, setting a new standard in the challenging realm of low-light image enhancement.
  arXiv  Detail & Related papers  (2024-09-03T06:19:03Z)
• Dual High-Order Total Variation Model for Underwater Image Restoration [13.789310785350484]
  Underwater image enhancement and restoration (UIER) is one crucial mode to improve the visual quality of underwater images. We propose an effective variational framework based on an extended underwater image formation model (UIFM) In our proposed framework, the weight factors-based color compensation is combined with the color balance to compensate for the attenuated color channels and remove the color cast.
  arXiv  Detail & Related papers  (2024-07-20T13:06:37Z)
• Inhomogeneous illumination image enhancement under ex-tremely low visibility condition [3.534798835599242]
  Imaging through dense fog presents unique challenges, with essential visual information crucial for applications like object detection and recognition obscured, thereby hindering conventional image processing methods. We introduce in this paper a novel method that adaptively filters background illumination based on Structural Differential and Integral Filtering (F) to enhance only vital signal information. Our findings demonstrate that our proposed method significantly enhances signal clarity under extremely low visibility conditions and out-performs existing techniques, offering substantial improvements for deep fog imaging applications.
  arXiv  Detail & Related papers  (2024-04-26T16:09:42Z)
• CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement [97.95330185793358]
  Low-light image enhancement (LLIE) aims to improve low-illumination images. Existing methods face two challenges: uncertainty in restoration from diverse brightness degradations and loss of texture and color information. We propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement.
  arXiv  Detail & Related papers  (2024-04-08T07:34:39Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments [51.58771256128329]
  This paper proposes a specialized dual-modulated learning framework that attempts to deeply dissect the nature of the low-light super-resolution task. We develop Illuminance-Semantic Dual Modulation (ISDM) components to enhance feature-level preservation of illumination and color details. Comprehensive experiments showcases the applicability and generalizability of our approach to diverse and challenging ultra-low-light conditions.
  arXiv  Detail & Related papers  (2023-09-11T06:55:32Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement [52.49231695707198]
  We investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Inspired by the color image formulation, we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. Our proposed method has surpassed the SOTA by 0.95dB in PSNR on LOL1000 dataset and 3.18% in mAP on ExDark dataset.
  arXiv  Detail & Related papers  (2021-03-19T04:00:27Z)
• Underwater Image Color Correction by Complementary Adaptation [0.0]
  We propose a novel approach for underwater image color correction based on a Tikhonov type optimization model in the CIELAB color space. Understood as a long-term adaptive process, our method effectively removes the underwater color cast and yields a balanced color distribution.
  arXiv  Detail & Related papers  (2020-10-21T03:59:22Z)
• Low-Light Maritime Image Enhancement with Regularized Illumination Optimization and Deep Noise Suppression [5.401654133604235]
  We propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods.
  arXiv  Detail & Related papers  (2020-08-09T17:05:23Z)

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.