Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition

Lim, Jaemoon; Heo, Minhyeok; Lee, Chul; Kim, Chang-Su

doi:10.1016/j.jvcir.2017.02.016

Cited by 41 publications

(8 citation statements)

References 39 publications

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“…It increases contrast effectively at low computational complexity, but it may over-enhance an image, resulting in contrast over-stretching, noise amplification, and contour artifacts. Various histogram methods [1], [3], [4], [23], [24] have been developed to overcome these problems. Second, parametric curve methods, such as gamma correction and logarithm mapping, use parametric curves as transformation functions between input and output pixel values.…”

Section: A Traditional Methodsmentioning

confidence: 99%

IceNet for Interactive Contrast Enhancement

2021

IEEE Access

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A CNN-based interactive contrast enhancement algorithm, called IceNet, is proposed in this paper, which enables a user to adjust image contrast easily according to his or her preference. Specifically, a user provides a parameter for controlling the global brightness and two types of scribbles to darken or brighten local regions in an image. Then, given these annotations, IceNet estimates a gamma map for the pixel-wise gamma correction. Finally, through color restoration, an enhanced image is obtained. The user may provide annotations iteratively to obtain a satisfactory image. IceNet is also capable of producing a personalized enhanced image automatically, which can serve as a basis for further adjustment if so desired. Moreover, to train IceNet effectively and reliably, we propose three differentiable losses. Extensive experiments demonstrate that IceNet can provide users with satisfactorily enhanced images.INDEX TERMS Interactive contrast enhancement, personalized contrast enhancement, convolutional neural network, and adaptive gamma correction.

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Section: A Traditional Methodsmentioning

confidence: 99%

IceNet for Interactive Contrast Enhancement

2021

IEEE Access

Self Cite

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“…Blind Image Spatial Quality Evaluator [35] (BRISQUE) is a no-reference IQA tool, proposed by Mittal et al, used in image enhancement contexts [36,37]. As opposed to the previous methods, BRISQUE is based on a set of Classical feature extraction procedures that computes a collection of 36 features per image.…”

Section: Image Quality Assessmentmentioning

confidence: 99%

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

et al. 2022

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Image Enhancement (IE) is an image processing procedure in which the image’s original information is improved, highlighting specific features to ease post-processing analyses by a human or machine. State-of-the-art image enhancement pipelines apply solutions to fixed and static constraints to solve specific issues in isolation. In this work, an IE system for image marketing is proposed, more precisely, real estate marketing, where the objective is to enhance the commercial appeal of the images, while maintaining a level of realism and similarity with the original image. This work proposes a generic image enhancement pipeline that combines state-of-the-art image processing filters, Machine Learning methods, and Evolutionary approaches, such as Genetic Programming (GP), to create a dynamic framework for Image Enhancement. The GP-based system is trained to optimize 4 metrics: Neural Image Assessment (NIMA) technical and BRISQUE, which evaluate the technical quality of the images; and NIMA aesthetics and PhotoILike, that evaluate the commercial attractiveness. It is shown that the GP model was able to find the best image quality enhancement (0.97 NIMA Aesthetics), while maintaining a high level of similarity with the original images (Structural Similarity Index Measure (SSIM) of 0.88). The framework has better performance according to the image quality metrics than the off-the-shelf image enhancement tool and the framework’s isolated parts.

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“…Image decomposition, which is also known as layer separation in other fields, has been steadily used for image restoration [26], image enhancement [27], and image fusion [28]. Image decomposition is an approach for separating an input image into two or more layers with different gradient and illumination characteristics.…”

Section: Image Decomposition In Deep Learning Frameworkmentioning

confidence: 99%

Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

Son

2021

Applied Sciences

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Layer decomposition to separate an input image into base and detail layers has been steadily used for image restoration. Existing residual networks based on an additive model require residual layers with a small output range for fast convergence and visual quality improvement. However, in inverse halftoning, homogenous dot patterns hinder a small output range from the residual layers. Therefore, a new layer decomposition network based on the Gaussian convolution model (GCM) and a structure-aware deblurring strategy is presented to achieve residual learning for both the base and detail layers. For the base layer, a new GCM-based residual subnetwork is presented. The GCM utilizes a statistical distribution, in which the image difference between a blurred continuous-tone image and a blurred halftoned image with a Gaussian filter can result in a narrow output range. Subsequently, the GCM-based residual subnetwork uses a Gaussian-filtered halftoned image as the input, and outputs the image difference as a residual, thereby generating the base layer, i.e., the Gaussian-blurred continuous-tone image. For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented. To remove the Gaussian blurring of the base layer, the SARDS uses the predicted base layer as the input, and outputs the deblurred version. To more effectively restore image structures such as lines and text, a new image structure map predictor is incorporated into the deblurring network to induce structure-adaptive learning. This paper provides a method to realize the residual learning of both the base and detail layers based on the GCM and SARDS. In addition, it is verified that the proposed method surpasses state-of-the-art methods based on U-Net, direct deblurring networks, and progressively residual networks.

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Contrast enhancement of noisy low-light images based on structure-texture-noise decomposition

Cited by 41 publications

References 39 publications

IceNet for Interactive Contrast Enhancement

IceNet for Interactive Contrast Enhancement

Towards Automatic Image Enhancement with Genetic Programming and Machine Learning

Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning

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