Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising
Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of e...
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| Veröffentlicht in: | Sensors (Basel, Switzerland) Jg. 21; H. 23; S. 7906 |
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| Abstract | Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately. |
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| AbstractList | Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately. Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately.Using a sensor in variable lighting conditions, especially very low-light conditions, requires the application of image enhancement followed by denoising to retrieve correct information. The limits of such a process are explored in the present paper, with the objective of preserving the quality of enhanced images. The LIP (Logarithmic Image Processing) framework was initially created to process images acquired in transmission. The compatibility of this framework with the human visual system makes possible its application to images acquired in reflection. Previous works have established the ability of the LIP laws to perform a precise simulation of exposure time variation. Such a simulation permits the enhancement of low-light images, but a denoising step is required, realized by using a CNN (Convolutional Neural Network). A main contribution of the paper consists of using rigorous tools (metrics) to estimate the enhancement reliability in terms of noise reduction, visual image quality, and color preservation. Thanks to these tools, it has been established that the standard exposure time can be significantly reduced, which considerably enlarges the use of a given sensor. Moreover, the contribution of the LIP enhancement and denoising step are evaluated separately. |
| Author | Carré, Maxime Jourlin, Michel |
| AuthorAffiliation | 2 Hubert Curien Laboratory, Jean Monnet University, 42000 Saint-Etienne, France 1 NT2I Company, 42000 Saint-Etienne, France; m.carre@nt2i.fr |
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| References | Gorgel (ref_1) 2010; 34 Jourlin (ref_7) 1998; 149 Panetta (ref_17) 2008; 38 Tian (ref_25) 2020; 131 Deng (ref_15) 2016; 55 ref_18 Jourlin (ref_28) 2021; 249 (ref_30) 2007; 7 ref_16 Brailean (ref_10) 1991; 4 Jourlin (ref_6) 1987; 6 Jourlin (ref_19) 2011; 168 Deng (ref_14) 2009; 18 Jourlin (ref_12) 1995; 41 ref_23 ref_22 ref_20 Deng (ref_13) 1995; 4 ref_3 Hecht (ref_11) 1924; 7 ref_29 ref_27 ref_26 ref_9 Nnolim (ref_21) 2018; 154 Jourlin (ref_8) 2001; 115 Buades (ref_24) 2005; 4 Gassenmaier (ref_2) 2021; 56 ref_5 ref_4 |
| References_xml | – ident: ref_9 – ident: ref_26 doi: 10.1007/978-3-319-24574-4_28 – volume: 18 start-page: 1135 year: 2009 ident: ref_14 article-title: An entropy interpretation of the logarithmic image processing model with application to contrast enhancement publication-title: IEEE Trans. Image Process. doi: 10.1109/TIP.2009.2016796 – ident: ref_22 doi: 10.1109/ICMCS.2014.6911247 – ident: ref_3 doi: 10.1109/SAMPTA.2017.8024474 – volume: 4 start-page: 2957 year: 1991 ident: ref_10 article-title: Evaluating the EM algorithm for image processing using a human visual fidelity criterion publication-title: Int. Conf. Acoust. Speech Signal Process. – volume: 249 start-page: 36 year: 2021 ident: ref_28 article-title: Image enhancement in the LIP framework and noise reduction with deep convolutional neural networks to produce high quality images from low light acquisitions publication-title: Sens. Transducers – volume: 115 start-page: 129 year: 2001 ident: ref_8 article-title: Logarithmic image processing: The mathematical and physical framework for the representation and processing of transmitted images publication-title: Adv. Imaging Electron. Phys. doi: 10.1016/S1076-5670(01)80095-1 – volume: 41 start-page: 225 year: 1995 ident: ref_12 article-title: Image dynamic range enhancement and stabilization in the context of the logarithmic image processing model publication-title: Signal Process. doi: 10.1016/0165-1684(94)00102-6 – volume: 38 start-page: 174 year: 2008 ident: ref_17 article-title: Human visual system-based image enhancement and logarithmic contrast measure publication-title: IEEE Trans. Syst. Man Cybern. B Cybern. doi: 10.1109/TSMCB.2007.909440 – ident: ref_4 doi: 10.1109/ICIP.2015.7351501 – ident: ref_18 – volume: 168 start-page: 65 year: 2011 ident: ref_19 article-title: Logarithmic image processing for color images publication-title: Adv. Imaging Electron. Phys. doi: 10.1016/B978-0-12-385983-9.00002-8 – ident: ref_23 – volume: 34 start-page: 993 year: 2010 ident: ref_1 article-title: A wavelet-based mammographic image denoising and enhancement with homomorphic filtering publication-title: J. Med. Syst doi: 10.1007/s10916-009-9316-3 – volume: 154 start-page: 192 year: 2018 ident: ref_21 article-title: An adaptive RGB colour enhancement formulation for logarithmic image processing-based algorithms publication-title: Optik doi: 10.1016/j.ijleo.2017.09.102 – ident: ref_16 doi: 10.1117/12.665693 – volume: 55 start-page: 253 year: 2016 ident: ref_15 article-title: The symmetric generalized LIP model and its application in dynamic range enhancement publication-title: J. Math. Imaging Vis. doi: 10.1007/s10851-015-0619-3 – volume: 149 start-page: 21 year: 1998 ident: ref_7 article-title: A model for logarithmic image processing publication-title: J. Microsc. doi: 10.1111/j.1365-2818.1988.tb04559.x – ident: ref_29 – volume: 7 start-page: 235 year: 1924 ident: ref_11 article-title: The visual discrimination of intensity and the Weber-Fechner law publication-title: J. Gen. Physiol. doi: 10.1085/jgp.7.2.235 – volume: 131 start-page: 251 year: 2020 ident: ref_25 article-title: Deep learning on image denoising: An overview publication-title: Neural Netw. doi: 10.1016/j.neunet.2020.07.025 – ident: ref_27 – volume: 56 start-page: 328 year: 2021 ident: ref_2 article-title: Application of a novel iterative denoising and image enhancement technique in T1-weighted precontrast and postcontrast gradient echo imaging of the abdomen. Improvement of image quality and diagnostic confidence publication-title: Investig. Radiol. doi: 10.1097/RLI.0000000000000746 – volume: 4 start-page: 490 year: 2005 ident: ref_24 article-title: A review of image denoising algorithms, with a new one publication-title: Multiscale Model. Simul. doi: 10.1137/040616024 – volume: 7 start-page: 4 year: 2007 ident: ref_30 article-title: Apex-additive system of photographic exposure publication-title: Issue – volume: 4 start-page: 506 year: 1995 ident: ref_13 article-title: The study of logarithmic image processing model and its application to image enhancement publication-title: IEEE Trans. Image Process. doi: 10.1109/83.370681 – ident: ref_5 doi: 10.1109/CVPR.2018.00347 – ident: ref_20 – volume: 6 start-page: 651 year: 1987 ident: ref_6 article-title: Logarithmic image processing publication-title: Acta Stereol. |
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| Title | Extending Camera’s Capabilities in Low Light Conditions Based on LIP Enhancement Coupled with CNN Denoising |
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