Image declipping: Saturation correction in single images

Karakaya, Diclehan; Ulucan, Oguzhan; Türkan, Mehmet

doi:10.1016/j.dsp.2022.103537

Cited by 5 publications

(1 citation statement)

References 23 publications

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“…There are numerous image processing methods designed to improve image quality by the removal of different kinds of distortions [ 1 , 2 , 3 , 4 , 5 , 6 ]. It is obvious that not every distortion can affect the entire image area; thus, every single pixel does not need to be processed.…”

Section: Introductionmentioning

confidence: 99%

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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A new approach to the evaluation of digital image denoising algorithms is presented. In the proposed method, the mean absolute error (MAE) is decomposed into three components that reflect the different cases of denoising imperfections. Moreover, aim plots are described, which are designed to be a very clear and intuitive form of presentation of the new decomposed measure. Finally, examples of the application of the decomposed MAE and the aim plots in the evaluation of impulsive noise removal algorithms are presented. The decomposed MAE measure is a hybrid of the image dissimilarity measure and detection performance measures. It provides information about sources of errors such as pixel estimation errors, unnecessary altered pixels, or undetected and uncorrected distorted pixels. It measures the impact of these factors on the overall correction performance. The decomposed MAE is suitable for the evaluation of algorithms that perform a detection of the distortion that affects only a certain fraction of the image pixels.

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Section: Introductionmentioning

confidence: 99%

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Maliński

2023

Sensors

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A Scale-space Approach for Surface Normal Vector Estimation from Depth Maps

Ulucan,

Ebner

2024

SN COMPUT. SCI.

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Surface normal vectors provide cues about the local geometric features of the scene which are utilized in many computer vision and computer graphics applications. Thus, the estimation of surface normals by utilizing structured range sensor data is an important research field. Thereupon, we propose a learning-free algorithm to estimate the surface normal vectors from depth maps. Our simple yet effective method relies on computations carried out in scale-space. Our main idea is to estimate the surface normals which cannot be properly computed in the finest scale from the coarser scales. Our method can estimate the surface normals even for images included in datasets that have challenging characteristics such as noisy real-world data or significantly large planar regions that either have a small or no gradient change. We analyze our algorithm’s performance by utilizing five benchmarks, namely, the MIT-Berkeley Intrinsic Images dataset, the New Tsukuba Dataset, the SceneNet RGB-D dataset, the IID-NORD dataset, and the NYU Depth Dataset V2, and by using two different evaluation strategies. According to the experimental results, our method can estimate surface normals efficiently without requiring neither complex computations nor huge amounts of data.

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An innovative 16-bit projection display based on quaternary hybrid light modulation

Pan,

Cao,

et al. 2024

Optics and Lasers in Engineering

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Image declipping: Saturation correction in single images

Cited by 5 publications

References 23 publications

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

Decomposed Dissimilarity Measure for Evaluation of Digital Image Denoising

A Scale-space Approach for Surface Normal Vector Estimation from Depth Maps

An innovative 16-bit projection display based on quaternary hybrid light modulation

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