Saturation-value based higher-order regularization for color image restoration

Jung, Mi Sook

doi:10.1007/s11045-023-00867-x

Cited by 2 publications

(1 citation statement)

References 54 publications

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“…In the literature, many color image processing models are proposed based on different color spaces, e.g. color image restoration [2,4,9,13,28,30,36], color image enhancement [9,15] based on RGB color space, color image restoration [8], color image segmentation [10,20,39] based on HSV color space, color image restoration [22], color image enhancement [1] based on CIELAB color space, color image enhancement [33], color image restoration [19,21,23,25,32,34], color image decomposition [26,37] based on opponent color space, etc.…”

Section: Introductionmentioning

confidence: 99%

Total Variations for Hue, Saturation, and Value of a Color Image

Wei Wang,

Chengyun Yang

2024

CSIAM-AM

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In this paper, we develop total variations for hue, saturation, and value of a color image, and we propose a novel hue-saturation-value total variation model for color image restoration. We first refine the hue formulation of a color image to make it mathematically and applicationally meaningful by assigning different hue values to different colors. We then develop the proposed hue-saturation-value total variation based on the conception of hue/saturation/value gradient. We investigate the dual formulation and the properties of the proposed hue-saturation-value total variation, and we finally propose a color image restoration model which is formulated by combing the proposed hue-saturation-value total variation regularization with the data-fitting term between the objective color image and the observed color image. We develop an efficient alternating iterative algorithm to solve the proposed optimization model in practice, and we give the convergence analysis of the proposed algorithm. Numerical examples are presented to demonstrate that the performance of the proposed hue-saturation-value total variation and the proposed color restoration model is better than that of other testing methods in terms of visual quality, peak signal-tonoise ratio (PSNR), structural similarity index (SSIM), and S-CIELAB color error.

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Section: Introductionmentioning

confidence: 99%

Total Variations for Hue, Saturation, and Value of a Color Image

Wei Wang,

Chengyun Yang

2024

CSIAM-AM

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Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise

Jung

2024

MATH

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<abstract><p>In this article, we propose a novel group-based sparse representation (GSR) model for restoring color images in the presence of multiplicative noise. This model consists of a convex data-fidelity term, and two regularizations including GSR and saturation-value-based total variation (SVTV). The data-fidelity term is suitable for handling heavy multiplicative noise. GSR enables the retention of textures and details while sufficiently removing noise in smooth regions without producing the staircase artifacts engendered by total variation-based models. Furthermore, we introduce a multi-color channel-based GSR that involves coupling between three color channels. This avoids the generation of color artifacts caused by decoupled color channel-based methods. SVTV further improves the visual quality of restored images by diminishing certain artifacts induced by patch-based methods. To solve the proposed nonconvex model and its subproblem, we exploit the alternating direction method of multipliers, which contributes to an efficient iterative algorithm. Numerical results demonstrate the outstanding performance of the proposed model compared to other existing models regarding visual aspect and image quality evaluation values.</p></abstract>

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Saturation-value based higher-order regularization for color image restoration

Cited by 2 publications

References 54 publications

Total Variations for Hue, Saturation, and Value of a Color Image

Total Variations for Hue, Saturation, and Value of a Color Image

Group sparse representation and saturation-value total variation based color image denoising under multiplicative noise

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