Towards the design of a consistent image contrast enhancement evaluation measure

Qureshi, Muhammad Ali; Beghdadi, Azeddine; Deriche, Mohamed

doi:10.1016/j.image.2017.08.004

Cited by 59 publications

(29 citation statements)

References 42 publications

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“…A higher RE value means a better edge restoration. Finally, in order to measure the global image contrast, we proposed to use Mutual Information based Contrast Measure (MICM) recommended in [ 46 ]. A lower MICM value indicates higher and better contrast in the images.…”

Section: Resultsmentioning

confidence: 99%

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Variational based smoke removal in laparoscopic images

et al. 2018

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BackgroundIn laparoscopic surgery, image quality can be severely degraded by surgical smoke, which not only introduces errors for the image processing algorithms (used in image guided surgery), but also reduces the visibility of the observed organs and tissues. To overcome these drawbacks, this work aims to remove smoke in laparoscopic images using an image preprocessing method based on a variational approach.MethodsIn this paper, we present the physical smoke model where the degraded image is separated into two parts: direct attenuation and smoke veil and propose an efficient variational-based desmoking method for laparoscopic images. To estimate the smoke veil, the proposed method relies on the observation that smoke veil has low contrast and low inter-channel differences. A cost function is defined based on this prior knowledge and is solved using an augmented Lagrangian method. The obtained smoke veil is then subtracted from the original degraded image, resulting in the direct attenuation part. Finally, the smoke free image is computed using a linear intensity transformation of the direct attenuation part.ResultsThe performance of the proposed method is evaluated quantitatively and qualitatively using three datasets: two public real smoked laparoscopic datasets and one generated synthetic dataset. No-reference and reduced-reference image quality assessment metrics are used with the two real datasets, and show that the proposed method outperforms the state-of-the-art ones. Besides, standard full-reference ones are employed with the synthetic dataset, and indicate also the good performance of the proposed method. Furthermore, the qualitative visual inspection of the results shows that our method removes smoke effectively from the laparoscopic images.ConclusionAll the obtained results show that the proposed approach reduces the smoke effectively while preserving the important perceptual information of the image. This allows to provide a better visualization of the operation field for surgeons and improve the image guided laparoscopic surgery procedure.

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Section: Resultsmentioning

confidence: 99%

“…c RE [ 45 ]. d MICM [ 46 ]. Note that, for the JNBM, only 500 frames are plotted to provide better illustration …”

Section: Resultsmentioning

confidence: 99%

Variational based smoke removal in laparoscopic images

et al. 2018

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“…For the subjective testing, we have used pairwise-comparison protocol. 12,13 For each observer, we randomly displayed all possible pair combinations of distorted videos, such that each pair had two videos from the same category and the same distortion type, with only difference being the severity level. This corresponded to 6 pair-wise comparisons per reference video for each distortion type.…”

Section: Subjective Testsmentioning

confidence: 99%

Towards a video quality assessment based framework for enhancement of laparoscopic videos

Khan

Beghdadi

Cheikh³

et al. 2020

Medical Imaging 2020: Image Perception, Observer Performance, and Technology Assessment

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Laparoscopic videos can be affected by different distortions which may impact the performance of surgery and introduce surgical errors. In this work, we propose a framework for automatically detecting and identifying such distortions and their severity using video quality assessment. There are three major contributions presented in this work (i) a proposal for a novel video enhancement framework for laparoscopic surgery; (ii) a publicly available database for quality assessment of laparoscopic videos evaluated by expert as well as non-expert observers and (iii) objective video quality assessment of laparoscopic videos including their correlations with expert and non-expert scores.

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“…In addition to the four IQA techniques used in the dataset labeling step, two more were used to evaluate the similarity between the ideal (original) and obtained (contrast enhanced) images: absolute mean brightness error (AMBE) [70] and visual information fidelity (VIF) [71]. According to [72], most of the existing contrast enhancement evaluation measures are not related to human perception of enhancement quality. Thus, we selected AMBE and VIF due to being full-reference techniques and exhibiting positive correlations with perceptual quality of contrast enhancement.…”

Section: Full-reference Image Quality Assessmentmentioning

confidence: 99%

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

Campos

Mastelini

Aguiar

et al. 2019

J Image Video Proc.

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Contrast enhancement algorithms have been evolved through last decades to meet the requirement of its objectives. Actually, there are two main objectives while enhancing the contrast of an image: (i) improve its appearance for visual interpretation and (ii) facilitate/increase the performance of subsequent tasks (e.g., image analysis, object detection, and image segmentation). Most of the contrast enhancement techniques are based on histogram modifications, which can be performed globally or locally. The Contrast Limited Adaptive Histogram Equalization (CLAHE) is a method which can overcome the limitations of global approaches by performing local contrast enhancement. However, this method relies on two essential hyperparameters: the number of tiles and the clip limit. An improper hyperparameter selection may heavily decrease the image quality toward its degradation. Considering the lack of methods to efficiently determine these hyperparameters, this article presents a learning-based hyperparameter selection method for the CLAHE technique. The proposed supervised method was built and evaluated using contrast distortions from well-known image quality assessment datasets. Also, we introduce a more challenging dataset containing over 6200 images with a large range of contrast and intensity variations. The results show the efficiency of the proposed approach in predicting CLAHE hyperparameters with up to 0.014 RMSE and 0.935 R 2 values. Also, our method overcomes both experimented baselines by enhancing image contrast while keeping its natural aspect.

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Towards the design of a consistent image contrast enhancement evaluation measure

Cited by 59 publications

References 42 publications

Variational based smoke removal in laparoscopic images

Variational based smoke removal in laparoscopic images

Towards a video quality assessment based framework for enhancement of laparoscopic videos

Machine learning hyperparameter selection for Contrast Limited Adaptive Histogram Equalization

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