MUIE/index.html at master · 1024AILab/MUIE · GitHub

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            <h1 class="title is-1 publication-title">WaveUIE: Efficient Wavelet-Domain Underwater Image Enhancement</h1>
            <div class="is-size-5 publication-authors">
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<!--                <a href="https://scholar.google.com/citations?user=SBoHvVQAAAAJ" target="_blank">Zhihao Chen</a><sup>1 </sup>,</span>-->
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<!--                  <a href="https://scholar.google.com/citations?user=qKxpHGcAAAAJ" target="_blank">Yiyuan Ge</a><sup>2,#</sup>,</span>-->
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<!--                    <a href="https://scholar.google.com/citations?user=GWF20_wAAAAJ" target="_blank">Ziyang Wang</a><sup>3</sup>,-->
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<!--                    <a href="https://scholar.google.com/citations?hl=zh-CN&user=V-6H56AAAAAJ" target="_blank">Lu Yang</a><sup>1</sup>,-->
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<!--                    <span class="author-block"><sup>1 </sup>Beijing University of Posts and Telecommunications,-->
<!--                      <sup>2 </sup>South China University of Technology <br>-->
<!--                      <sup>3 </sup>University of Oxford,-->
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        <h2 class="title is-3">🌊✨ WaveUIE enhances raw YouTube wild underwater video into clear visuals ⬅️➡️ </h2>
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          <p>
            Underwater image enhancement remains challenging in computer vision due to its complex and diverse degradation phenomena. Existing state-of-the-art methods mainly focus on spatial domain modelling and demonstrate superiority, but they fall short in two key aspects: 1) underutilization of frequency domain features, and 2) insufficient exploitation of the relationship between the positive and negative samples during the training. To overcome these limitations, we propose a wavelet-driven framework called WaveUIE. Specifically, our method decomposes images into high/low-frequency components via the wavelet transform. To better mine the features of these two component patterns, a high-frequency enhancement block (HFEBlock) is introduced to restore fine textures of the high-frequency component. In addition, we also introduce a low-frequency enhancement block (LFEBlock) to effectively eliminates color casts and blur of the low-frequency features. Moreover, we design a dynamic frequency fusion block (DFFBlock), which orchestrates cross-frequency interactions by first calibrating high-frequency data with the enhanced low-frequency features, and then synthesising them into coherent visual outputs. Finally, we design a wavelet fine-grained contrastive policy (WFCPolicy) to decompose images into four sub-bands and impose contrastive constraints on the enhanced result (anchor) within the sub-band space. These constraints pull the anchor closer to the ground-truth (positive samples) while pushing it away from the original degraded image (negative samples), thereby explicitly leveraging the degradation features present in different frequency sub-bands to improve enhancement performance. Experimental evaluations demonstrate state-of-the-art performance across multiple benchmarks, with significant improvements in both SSIM and UIQM metrics over existing methods.
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      <h2 class="title is-3">Frequency-Domain Motivation</h2>
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            <img src="static/images/fig1.png" alt="Wavelet Domain Analysis">
            <figcaption class="has-text-centered is-italic">
              Comparative wavelet analysis revealing two critical observations:
              (1) Low-frequency components (LL-subband) dominate color distribution
              and global structure preservation, while (2) high-frequency components
              (HL/HH/LH-subbands) exhibit localized impact on texture details.
              Hybrid reconstruction experiments demonstrate that <strong>low-frequency
              substitution significantly alters RGB histograms</strong> whereas
              <strong>high-frequency exchange preserves overall color characteristics</strong>.
            </figcaption>
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            <img src="static/images/fig2.png" alt="WaveUIE Framework">
            <figcaption class="has-text-centered is-italic">
              Architecture of WaveUIE, directly motivated by our wavelet-domain observations:
              (1) <strong>LFEBlock</strong> conducts global compensation on low-frequency components
              (dominant in color/structure), (2) <strong>HFEBlock</strong> performs local refinement
              of high-frequency details (minor texture residuals), (3) <strong>DFFBlock</strong>
              orchestrates cross-frequency guidance where enhanced low-frequency features
              calibrate high-frequency restoration, followed by coherent multi-band fusion.
            </figcaption>
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            <img src="static/images/vis1.png" alt="UIEB Test Results">
            <figcaption class="has-text-centered is-italic">
              Visual comparisons on underwater images sampled from <strong>UIEB</strong> dataset.
              (a) RAWS. (b) Fusion. (c) WaterNet. (d) UWCNN-Type I.
              (e) UIEC²Net. (f) PUIE-Net. (g) U-Shape.
              (h) P2CNet. (i) Proposed WaveUIE. (j) Ground truth.
            </figcaption>
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      <!-- UIEB Challenge Results -->
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            <img src="static/images/vis2.png" alt="UIEB Challenge Results">
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              Visual comparisons on <strong>UIEB Challenge</strong> dataset.
              (a) RAWS. (b) Fusion. (c) WaterNet. (d) UWCNN-Type I.
              (e) UIEC²Net. (f) PUIE-Net. (g) U-Shape.
              (h) P2CNet. (i) Proposed WaveUIE.
            </figcaption>
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      <!-- LSUI Results -->
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            <img src="static/images/vis3.png" alt="LSUI Results">
            <figcaption class="has-text-centered is-italic">
              Visual comparisons on <strong>LSUI</strong> dataset.
              (a) RAWS. (b) Fusion. (c) WaterNet. (d) UWCNN-Type I.
              (e) UIEC²Net. (f) PUIE-Net. (g) U-Shape.
              (h) P2CNet. (i) Proposed WaveUIE. (j) Ground truth.
            </figcaption>
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<!--        <strong>References:</strong><br>-->
<!--        [1] Ancuti et al. (2012)  -->
<!--        [2] Li et al. (2019)  -->
<!--        [3] Li et al. (2020)  -->
<!--        [4] Wang et al. (2021)<br>-->
<!--        [5] Fu et al. (2022)  -->
<!--        [6] Peng et al. (2023)  -->
<!--        [7] Rao et al. (2023)-->
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