Contrast Sensitivity Based Multiscale Base–Detail Separation for Enhanced HDR Imaging

Kwon, Hyunwook; Lee, Sung-Hak

doi:10.3390/app10072513

Cited by 5 publications

(6 citation statements)

References 22 publications

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“…Visual signals (digital images or videos) undergo a wide variety of distortions during acquisition, compression, transmission, and storage. Thus, image quality assessment (IQA) is crucial to predict the quality of digital images in many applications, such as compression, communication, printing, display, analysis, registration, restoration, and enhancement [ 1 , 2 , 3 , 4 ]. Generally, it can be used in benchmarking any image processing algorithms.…”

Section: Introductionmentioning

confidence: 99%

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Varga

2020

J. Imaging

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The goal of no-reference image quality assessment (NR-IQA) is to predict the quality of an image as perceived by human observers without using any pristine, reference images. In this study, an NR-IQA algorithm is proposed which is driven by a novel feature vector containing statistical and perceptual features. Different from other methods, normalized local fractal dimension distribution and normalized first digit distributions in the wavelet and spatial domains are incorporated into the statistical features. Moreover, powerful perceptual features, such as colorfulness, dark channel feature, entropy, and mean of phase congruency image, are also incorporated to the proposed model. Experimental results on five large publicly available databases (KADID-10k, ESPL-LIVE HDR, CSIQ, TID2013, and TID2008) show that the proposed method is able to outperform other state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Varga

2020

J. Imaging

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“…For existing methods, such as Reinhard [5] and L1L0, the tone increases across the brightness range, resulting in saturation in bright areas and reduced visibility in dark areas. Although iCAM06 and the method by Kwon et al [12]. ensure visibility in dark areas, color distortions, such as saturation, occur in some areas, and an overall reddish tone appears in the image.…”

Section: Resultsmentioning

confidence: 99%

“…However, there may be a detail reduction problem due to feature-based fusion with fixed-edge stopping functions. Hence, Kwon et al proposed using the characteristics of the contrast sensitivity function as a base-detail separation method and detail compensation technique to preserve iCAM06 edge components [12].…”

Section: Introductionmentioning

confidence: 99%

Retinex Jointed Multiscale CLAHE Model for HDR Image Tone Compression

Kim,

Son,

Lee

2024

Mathematics

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Tone-mapping algorithms aim to compress a wide dynamic range image into a narrower dynamic range image suitable for display on imaging devices. A representative tone-mapping algorithm, Retinex theory, reflects color constancy based on the human visual system and performs dynamic range compression. However, it may induce halo artifacts in some areas or degrade chroma and detail. Thus, this paper proposes a Retinex jointed multiscale contrast limited adaptive histogram equalization method. The proposed algorithm reduces localized halo artifacts and detail loss while maintaining the tone-compression effect via high-scale Retinex processing. A performance comparison of the experimental results between the proposed and existing methods confirms that the proposed method effectively reduces the existing problems and displays better image quality.

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“…Since this approach does not consider the camera response properties, it faces the challenges of over-and under-enhancement [1]. These studies [44][45][46][47] combined contrast optimization and detail enhancement to perform the HDR tone mapping and reconstruct HDR images with over saturation and undesirable detail suppression. This study [48] uses global histogram correction for tone mapping.…”

Section: Methodsmentioning

confidence: 99%

Fast Single-Image HDR Tone-Mapping by Avoiding Base Layer Extraction

Fahim

Jung

2020

Sensors

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The tone-mapping algorithm compresses the high dynamic range (HDR) information into the standard dynamic range for regular devices. An ideal tone-mapping algorithm reproduces the HDR image without losing any vital information. The usual tone-mapping algorithms mostly deal with detail layer enhancement and gradient-domain manipulation with the help of a smoothing operator. However, these approaches often have to face challenges with over enhancement, halo effects, and over-saturation effects. To address these challenges, we propose a two-step solution to perform a tone-mapping operation using contrast enhancement. Our method improves the performance of the camera response model by utilizing the improved adaptive parameter selection and weight matrix extraction. Experiments show that our method performs reasonably well for overexposed and underexposed HDR images without producing any ringing or halo effects.

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Contrast Sensitivity Based Multiscale Base–Detail Separation for Enhanced HDR Imaging

Cited by 5 publications

References 22 publications

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

No-Reference Image Quality Assessment Based on the Fusion of Statistical and Perceptual Features

Retinex Jointed Multiscale CLAHE Model for HDR Image Tone Compression

Fast Single-Image HDR Tone-Mapping by Avoiding Base Layer Extraction

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