Colour Constant Image Sharpening

Alsam, Ali

doi:10.1109/icpr.2010.1104

Cited by 8 publications

(3 citation statements)

References 4 publications

(3 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several techniques have been proposed to retain more information from the original color image. They roughly are categorized into two groups: approaches based on the use of local information in the image [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 ] and based on global information [ 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 ]. The local approaches enhance the final grayscale output with local information, like chrominance edges and high-frequency image details, while global approaches combine both local and non-local contrast, structure, and illumination for color compression [ 35 ].…”

Section: Related Workmentioning

confidence: 99%

An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function

Wu,

Eising,

Glavin

et al. 2024

J. Imaging

View full text Add to dashboard Cite

Image decolorization is an image pre-processing step which is widely used in image analysis, computer vision, and printing applications. The most commonly used methods give each color channel (e.g., the R component in RGB format, or the Y component of an image in CIE-XYZ format) a constant weight without considering image content. This approach is simple and fast, but it may cause significant information loss when images contain too many isoluminant colors. In this paper, we propose a new method which is not only efficient, but also can preserve a higher level of image contrast and detail than the traditional methods. It uses the information from the cumulative distribution function (CDF) of the information in each color channel to compute a weight for each pixel in each color channel. Then, these weights are used to combine the three color channels (red, green, and blue) to obtain the final grayscale value. The algorithm works in RGB color space directly without any color conversion. In order to evaluate the proposed algorithm objectively, two new metrics are also developed. Experimental results show that the proposed algorithm can run as efficiently as the traditional methods and obtain the best overall performance across four different metrics.

show abstract

Section: Related Workmentioning

confidence: 99%

An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function

Wu,

Eising,

Glavin

et al. 2024

J. Imaging

View full text Add to dashboard Cite

show abstract

“…The human visual system (HVS) is incredibly versatile in adjusting to changes in illumination sources [1,2]. The HVS has the ability to automatically modulate the receptive field size of visual neurons and achieve color constancy effortlessly.…”

Section: Introductionmentioning

confidence: 99%

CVCC Model: Learning-Based Computer Vision Color Constancy with RiR-DSN Architecture

Choi

2023

Sensors

View full text Add to dashboard Cite

To achieve computer vision color constancy (CVCC), it is vital but challenging to estimate scene illumination from a digital image, which distorts the true color of an object. Estimating illumination as accurately as possible is fundamental to improving the quality of the image processing pipeline. CVCC has a long history of research and has significantly advanced, but it has yet to overcome some limitations such as algorithm failure or accuracy decreasing under unusual circumstances. To cope with some of the bottlenecks, this article presents a novel CVCC approach that introduces a residual-in-residual dense selective kernel network (RiR-DSN). As its name implies, it has a residual network in a residual network (RiR) and the RiR houses a dense selective kernel network (DSN). A DSN is composed of selective kernel convolutional blocks (SKCBs). The SKCBs, or neurons herein, are interconnected in a feed-forward fashion. Every neuron receives input from all its preceding neurons and feeds the feature maps into all its subsequent neurons, which is how information flows in the proposed architecture. In addition, the architecture has incorporated a dynamic selection mechanism into each neuron to ensure that the neuron can modulate filter kernel sizes depending on varying intensities of stimuli. In a nutshell, the proposed RiR-DSN architecture features neurons called SKCBs and a residual block in a residual block, which brings several benefits such as alleviation of the vanishing gradients, enhancement of feature propagation, promotion of the reuse of features, modulation of receptive filter sizes depending on varying intensities of stimuli, and a dramatic drop in the number of parameters. Experimental results highlight that the RiR-DSN architecture performs well above its state-of-the-art counterparts, as well as proving to be camera- and illuminant-invariant.

show abstract

“…The human eye can effectively adjust to changes in visual conditions and different illuminations of the scene [1], [2]. The well-acknowledged image processing problem of computational color constancy tries to mimic this ability in digital cameras [3].…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo Dropout Ensembles for Robust Illumination Estimation

Laakom¹,

Raitoharju²,

Iosifidis³

et al. 2020

Preprint

View full text Add to dashboard Cite

Computational color constancy is a preprocessing step used in many camera systems. The main aim is to discount the effect of the illumination on the colors in the scene and restore the original colors of the objects. Recently, several deep learning-based approaches have been proposed to solve this problem and they often led to state-of-the-art performance in terms of average errors. However, for extreme samples, these methods fail and lead to high errors. In this paper, we address this limitation by proposing to aggregate different deep learning methods according to their output uncertainty. We estimate the relative uncertainty of each approach using Monte Carlo dropout and the final illumination estimate is obtained as the sum of the different model estimates weighted by the log-inverse of their corresponding uncertainties. The proposed framework leads to state-of-the-art performance on INTEL-TAU dataset.

show abstract

Colour Constant Image Sharpening

Cited by 8 publications

References 4 publications

An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function

An Efficient and Effective Image Decolorization Algorithm Based on Cumulative Distribution Function

CVCC Model: Learning-Based Computer Vision Color Constancy with RiR-DSN Architecture

Monte Carlo Dropout Ensembles for Robust Illumination Estimation

Contact Info

Product

Resources

About