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Image decomposition fusion method based on sparse representation and neural network.

Lihong Chang, Xiangchu Feng, Rui Zhang

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    |October 20, 2017
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    Summary
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    This study introduces a novel image fusion method using a cartoon + texture dictionary pair and deep neural network combination (DNNC) for improved denoising and fusion of noisy images. The proposed alternating denoising and fusion strategy significantly enhances image quality compared to existing methods.

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    Area of Science:

    • Computer Vision
    • Image Processing
    • Signal Processing

    Background:

    • Existing sparse representation models often perform image fusion and denoising simultaneously.
    • This can limit performance, especially for noisy or complex image datasets.

    Purpose of the Study:

    • To develop an advanced image fusion method that separates denoising and fusion steps for enhanced performance.
    • To introduce a novel approach utilizing a cartoon + texture dictionary pair and deep neural network combination (DNNC).

    Main Methods:

    • The proposed method employs an alternating denoising and fusion strategy in three main steps: initial denoising, dictionary-pair-based fusion, and final network-based fusion.
    • Source images are denoised separately using internal/external methods.
    • Fusion is performed using external/internal cartoon and texture dictionary pairs, yielding E-CTSR and I-CTSR.
    • These results are then combined using DNNC to produce the final EI-CTSR.

    Main Results:

    • The proposed EI-CTSR method outperforms standalone E-CTSR and I-CTSR methods.
    • EI-CTSR demonstrates superior performance compared to state-of-the-art methods like sparse representation (SR) and adaptive sparse representation (ASR) for isomorphic images.
    • E-CTSR shows better results than SR and ASR for heterogeneous multi-mode images.

    Conclusions:

    • The proposed alternating denoising and fusion approach with a cartoon + texture dictionary pair and DNNC offers significant improvements in image fusion and denoising.
    • This method provides a robust solution for both isomorphic and heterogeneous multi-mode noisy images.