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While the definition of \u201cimage quality\u201d for computer vision may be ill-defined, what is clear is that the configuration of the image signal processing pipeline is the key factor in controlling the image quality for computer vision. This paper is partly review and partly positional with demonstration of several preliminary results promising for future research. As such, we give an overview of what is an<\/jats:p>","DOI":"10.3390\/jimaging5100078","type":"journal-article","created":{"date-parts":[[2019,9,25]],"date-time":"2019-09-25T03:51:18Z","timestamp":1569383478000},"page":"78","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Overview and Empirical Analysis of ISP Parameter Tuning for Visual Perception in Autonomous Driving"],"prefix":"10.3390","volume":"5","author":[{"given":"Lucie","family":"Yahiaoui","sequence":"first","affiliation":[{"name":"Valeo Vision Systems, Dunmore Road, Tuam, Co. Galway H54 Y276, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan","family":"Horgan","sequence":"additional","affiliation":[{"name":"Valeo Vision Systems, Dunmore Road, Tuam, Co. Galway H54 Y276, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Brian","family":"Deegan","sequence":"additional","affiliation":[{"name":"Valeo Vision Systems, Dunmore Road, Tuam, Co. Galway H54 Y276, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Senthil","family":"Yogamani","sequence":"additional","affiliation":[{"name":"Valeo Vision Systems, Dunmore Road, Tuam, Co. Galway H54 Y276, Ireland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ciar\u00e1n","family":"Hughes","sequence":"additional","affiliation":[{"name":"Valeo Vision Systems, Dunmore Road, Tuam, Co. 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P.910: Subjective Video Quality Assessment Methods for Multimedia Applications, International Telecommunications Union."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Hertel, D.W., and Chang, E. (2007, January 13\u201315). Image quality standards in automotive vision applications. Proceedings of the 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey.","DOI":"10.1109\/IVS.2007.4290148"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Winterlich, A., Zlokolica, V., Denny, P., Kilmartin, L., Glavin, M., and Jones, E. (2013, January 3\u20135). A saliency weighted no-reference perceptual blur metric for the automotive environment. Proceedings of the 2013 Fifth International Workshop on Quality of Multimedia Experience (QoMEX), Klagenfurt am W\u00f6rthersee, Austria.","DOI":"10.1109\/QoMEX.2013.6603238"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.imavis.2017.07.002","article-title":"Computer vision in automated parking systems: Design, implementation and challenges","volume":"68","author":"Heimberger","year":"2017","journal-title":"Image Vis. Comput."},{"key":"ref_7","unstructured":"Johnson, J. (1958). Analysis of image forming systems. Image Intensifier Symposium, AD220160, Warfare Electrical Engineering Department, U.S. Army Research and Development Laboratoires."},{"key":"ref_8","unstructured":"Velichko, S. (2019, January 17\u201321). Intelligent Sensors For Autonomous Driving. Proceedings of the Automotive Forum of International Solid-State Circuits Conference (ISSCC), San Francisco, CA, USA."},{"key":"ref_9","unstructured":"Gomez, A. (2019, January 14\u201316). ISP Optimization for ML\/CV Automotive Applications. Proceedings of the AutoSens Conference, Detroit, MI, USA."},{"key":"ref_10","unstructured":"Somayaji, M. (2019, January 14\u201316). Tuning Image Processing Pipelines for Automotive Use. Proceedings of the AutoSens Conference, Detroit, MI, USA."},{"key":"ref_11","unstructured":"Yahiaoui, L., Hughes, C., Horgan, J., Deegan, B., Denny, P., and Yogamani, S. (2019, January 13\u201317). Optimization of ISP parameters for object detection algorithms. Proceedings of the Electronic Imaging, Autonomous Vehicles and Machines Conference, Burlingame, CA, USA."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Chen, J., Cranton, W., and Fihn, M. (2016). Imaging for the Automotive Environment. Handbook of Visual Display Technology, Springer.","DOI":"10.1007\/978-3-642-35947-7"},{"key":"ref_13","unstructured":"Watson, A.B. (1993). What\u2019s wrong with mean-squared error? Digital Images and Human Vision, MIT Press."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"2959","DOI":"10.1109\/26.477498","article-title":"Image quality meaures and their performance","volume":"43","author":"Eskicioglu","year":"1995","journal-title":"IEEE Trans. Commun."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"525","DOI":"10.1109\/TIT.1974.1055250","article-title":"The effects of a visual fidelity criterion on the encoding of images","volume":"4","author":"Mannos","year":"1974","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1016\/S0165-1684(98)00124-8","article-title":"Perceptual quality metrics applied to still image compression","volume":"70","author":"Eckert","year":"1998","journal-title":"Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1109\/97.995823","article-title":"A universal image quality index","volume":"9","author":"Wang","year":"2002","journal-title":"IEEE Signal Process. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIP.2003.819861","article-title":"Image Quality Assessment: From Error Visibility to Structural Similarity","volume":"13","author":"Wang","year":"2004","journal-title":"IEEE Trans. Image Process."},{"key":"ref_19","unstructured":"Wang, Z., Simoncelli, E., and Bovik, A. (2003, January 9\u201312). Multiscale structural similarity for image quality assessment. Proceedings of the Conference on Signals, Systems and Computers, Pacific Grove, CA, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"2117","DOI":"10.1109\/TIP.2005.859389","article-title":"An information fidelity criterion for image quality assessment using natural scene statistics","volume":"14","author":"Sheikh","year":"2005","journal-title":"IEEE Trans. Image Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"430","DOI":"10.1109\/TIP.2005.859378","article-title":"Image information and visual quality","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1109\/TIP.2007.901820","article-title":"A wavelet-based visual signal-to-noise ratio for natural images","volume":"16","author":"Chandler","year":"2007","journal-title":"IEEE Trans. Image Process."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, R., Isola, P., and Efros, A. (2016, January 11\u201314). Colorful image colorization. Proceedings of the European Conference on Computer Vision (ECCV), Amsterdam, The Netherlands.","DOI":"10.1007\/978-3-319-46487-9_40"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1109\/TIP.2011.2147325","article-title":"Blind image quality assessment: From natural scene statistics to perceptual quality","volume":"20","author":"Moorthy","year":"2011","journal-title":"IEEE Trans. Image Process."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"4695","DOI":"10.1109\/TIP.2012.2214050","article-title":"No-reference image quality assessment in the spatial domain","volume":"21","author":"Mittal","year":"2012","journal-title":"IEEE Trans. Image Process."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kingdom, F., and Prins, N. (2016). Psychophysics: A Practical Introduction, Academic Press. [2nd ed.].","DOI":"10.1016\/B978-0-12-407156-8.00001-3"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Lu, Z.L., and Dosher, B. (2013). Visual Psychophysics: From Laboratory to Theory, MIT Press.","DOI":"10.7551\/mitpress\/9780262019453.001.0001"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2280","DOI":"10.1109\/TPAMI.2018.2849989","article-title":"PsyPhy: A Psychophysics Driven Evaluation Framework for Visual Recognition","volume":"41","author":"RichardWebster","year":"2019","journal-title":"IEEE Trans. Pat. Anal. Mach. Intel."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"061114","DOI":"10.1117\/1.JEI.23.6.061114","article-title":"Performance optimization for pedestrian detection on degraded video using natural scene statistics","volume":"23","author":"Winterlich","year":"2014","journal-title":"J. Electron. Imaging"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Pezzementi, Z., Tabor, T., Yim, S., Chang, J., Drozd, B., Guttendorf, D., Wagner, M., and Koopman, P. (2018, January 6\u20138). Putting image manipulations in context: Robustness testing for safe perception. Proceedings of the IEEE International Symposium on Safety, Security, and Rescue Robotics (SSRR), Philadelphia, PA, USA.","DOI":"10.1109\/SSRR.2018.8468619"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Su, J., Vargas, D.V., and Sakurai, K. (2019). One pixel attack for fooling deep neural networks. IEEE Trans. Evolut. Comput.","DOI":"10.1109\/TEVC.2019.2890858"},{"key":"ref_32","unstructured":"Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I.J., and Fergus, R. (2014). Intriguing properties of neural networks. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Buckler, M., Jayasuriya, S., and Sampson, A. (2017, January 22\u201329). Reconfiguring the imaging pipeline for computer vision. Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy.","DOI":"10.1109\/ICCV.2017.111"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1023\/B:VISI.0000029664.99615.94","article-title":"Distinctive Image Features from Scale-Invariant Keypoints","volume":"60","author":"Lowe","year":"2004","journal-title":"Int. J. Comput. Vision"},{"key":"ref_35","unstructured":"Blasinski, H., Farrell, J., Lian, T., Liu, Z., and Wandell, B. (February, January 28). Optimizing Image Acquisition Systems for Autonomous Driving. Proceedings of the Electronic Imaging, Photography, Mobile, and Immersive Imaging Conference, Burlingame, CA, USA."},{"key":"ref_36","first-page":"2","article-title":"Shading correction: compensation for illumination and sensor inhomogeneities","volume":"14","author":"Young","year":"2001","journal-title":"Curr. Protoc. Cytom."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"460","DOI":"10.1109\/30.468045","article-title":"Automatic white balance for digital still camera","volume":"41","author":"Chen","year":"1995","journal-title":"IEEE Trans. Consum. Electron."},{"key":"ref_38","unstructured":"SangHyun Park, G.K., and Jeon, J. (2009, January 15\u201318). The method of auto exposure control for low-end digital camera. Proceedings of the 11th International Conference on Advanced Communication Technology, Phoenix Park, Korea."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1057","DOI":"10.1109\/TIM.2004.831494","article-title":"Automatic gain control for image-intensified camera","volume":"53","author":"Fowler","year":"2004","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2805","DOI":"10.1109\/TNNLS.2018.2886017","article-title":"Adversarial Examples: Attacks and Defenses for Deep Learning","volume":"30","author":"Yuan","year":"2019","journal-title":"IEEE Trans. Neur. Net. Lear."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yoo, Y., Lee, S.D., Choe, W., and Kim, C.Y. (2007, January 20). CMOS image sensor noise reduction method for image signal processor in digital cameras and camera phones. Proceedings of the Digital Photography, San Jose, CA, USA.","DOI":"10.1117\/12.702758"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/S1076-5670(10)62005-8","article-title":"Comparison of Color Demosaicing Methods","volume":"162","author":"Losson","year":"2010","journal-title":"Adv. Imag. Electron. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Takahashi, K., Monno, Y., Tanaka, M., and Okutomi, M. (2016, January 25\u201328). Effective color correction pipeline for a noisy image. Proceedings of the 2016 IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA.","DOI":"10.1109\/ICIP.2016.7533111"},{"key":"ref_44","unstructured":"Morris, T. (2004). Computer Vision and Image Processing, Palgrave Macmillan Ltd."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Horgan, J., Hughes, C., McDonald, J., and Yogamani, S. (2015, January 15\u201318). Vision-based driver assistance systems: Survey, taxonomy and advances. Proceedings of the 2015 IEEE 18th International Conference on Intelligent Transportation Systems, Las Palmas, Spain.","DOI":"10.1109\/ITSC.2015.329"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hartley, R., and Zisserman, A. (2003). Multiple View Geometry in Computer Vision, Cambridge University Press.","DOI":"10.1017\/CBO9780511811685"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Tareen, S.A.K., and Saleem, Z. (2018, January 3\u20134). A comparative analysis of SIFT, SURF, KAZE, AKAZE, ORB, and BRISK. Proceedings of the 2018 International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), Sukkur, Pakistan.","DOI":"10.1109\/ICOMET.2018.8346440"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Tang, Z., and Boukerche, A. (2018, January 20\u201324). An Improved Algorithm for Road Markings Detection with SVM and ROI Restriction: Comparison with a Rule-Based Model. Proceedings of the International Conference on Communications (ICC), Kansas City, MO, USA.","DOI":"10.1109\/ICC.2018.8422412"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Li, L., Luo, W., and Wang, K. (2018). Lane Marking Detection and Reconstruction with Line-Scan Imaging Data. Sensors, 18.","DOI":"10.3390\/s18051635"},{"key":"ref_50","unstructured":"Jung, H.G., Kim, D.S., Yoon, P.J., and Kim, J. (2006, January 13\u201315). Parking slot markings recognition for automatic parking assist system. Proceedings of the 2006 IEEE Intelligent Vehicles Symposium, Tokyo, Japan."},{"key":"ref_51","unstructured":"Liu, Y.C., Lin, K.Y., and Chen, Y.S. (2008, January 18\u201320). Bird\u2019s-eye view vision system for vehicle surrounding monitoring. Proceedings of the International Conference on Robot Vision (RobVis), Auckland, New Zealand."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Jung, H.G., Kim, D.S., Yoon, P.J., and Kim, J. (2006, January 17\u201319). Structure Analysis Based Parking Slot Marking Recognition For Semi-Automatic Parking System. Proceedings of the Joint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition, Hong Kong, China.","DOI":"10.1007\/11815921_42"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Su, B., and Lu, S. (2014, January 1\u20134). A System for Parking Lot Marking Detection. Proceedings of the Pacific-Rim Conference on Multimedia (PCM), Kuching, Malaysia.","DOI":"10.1007\/978-3-319-13168-9_30"},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The KITTI dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Chavan, A., and Yogamani, S.K. (2012, January 5\u20138). Real-time DSP implementation of pedestrian detection algorithm using HOG features. Proceedings of the 12th International Conference on ITS Telecommunications, Taipei, Taiwan.","DOI":"10.1109\/ITST.2012.6425196"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S., and Jagersand, M. (2018, January 7\u201310). RTSeg: Real-time semantic segmentation comparative study. Proceedings of the 25th IEEE International Conference on Image Processing (ICIP), Athens, Greece.","DOI":"10.1109\/ICIP.2018.8451495"},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T. (2016). Flownet 2.0: Evolution of optical flow estimation with deep networks. arXiv.","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Siam, M., Mahgoub, H., Zahran, M., Yogamani, S., Jagersand, M., and El-Sallab, A. (2018, January 4\u20137). Modnet: Motion and appearance based moving object detection network for autonomous driving. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569744"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Kumar, V.R., Milz, S., Witt, C., Simon, M., Amende, K., Petzold, J., Yogamani, S., and Pech, T. (2018, January 4\u20137). Monocular fisheye camera depth estimation using sparse lidar supervision. Proceedings of the 2018 21st International Conference on Intelligent Transportation Systems (ITSC), Maui, HI, USA.","DOI":"10.1109\/ITSC.2018.8569665"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Milz, S., Arbeiter, G., Witt, C., Abdallah, B., and Yogamani, S. (2018, January 8\u201322). Visual SLAM for Automated Driving: Exploring the Applications of Deep Learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPRW.2018.00062"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Uricar, M., Krizek, P., Sistu, G., and Yogamani, S. (2019, January 27\u201330). SoilingNet: Soiling Detection on Automotive Surround-View Cameras. Proceedings of the IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917178"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Sistu, G., Leang, I., Chennupati, S., Milz, S., Yogamani, S., and Rawashdeh, S. (2019, January 27\u201330). NeurAll: Towards a Unified Model for Visual Perception in Automated Driving. Proceedings of the IEEE Intelligent Transportation Systems Conference, Auckland, New Zealand.","DOI":"10.1109\/ITSC.2019.8917043"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Valada, A., Oliveira, G.L., Brox, T., and Burgard, W. (2016, January 3\u20136). Deep Multispectral Semantic Scene Understanding of Forested Environments using Multimodal Fusion. Proceedings of the International Symposium on Experimental Robotics (ISER), Tokyo, Japan.","DOI":"10.1007\/978-3-319-50115-4_41"},{"key":"ref_64","unstructured":"Bonanni, T.M., Pennisi, A., Bloisi, D., Iocchi, L., and Nardi, D. (2013, January 3). Human-robot collaboration for semantic labeling of the environment. Proceedings of the 3rd Workshop on Semantic Perception, Mapping and Exploration, Anchorage, AL, USA."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Vineet, V., Miksik, O., Lidegaard, M., Nie\u00dfner, M., Golodetz, S., Prisacariu, V.A., K\u00e4hler, O., Murray, D.W., Izadi, S., and Perez, P. (2015, January 26\u201330). Incremental Dense Semantic Stereo Fusion for Large-Scale Semantic Scene Reconstruction. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Seattle, WA, USA.","DOI":"10.1109\/ICRA.2015.7138983"},{"key":"ref_66","doi-asserted-by":"crossref","unstructured":"Kundu, A., Li, Y., Dellaert, F., Li, F., and Rehg, J.M. (2014, January 6\u201312). Joint semantic segmentation and 3d reconstruction from monocular video. Proceedings of the European Conference on Computer Vision, Zurich, Switzerland.","DOI":"10.1007\/978-3-319-10599-4_45"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"\u00c7i\u00e7ek, \u00d6., Abdulkadir, A., Lienkamp, S.S., Brox, T., and Ronneberger, O. (2016, January 17\u201321). 3D U-Net: Learning dense volumetric segmentation from sparse annotation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_49"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhu, W., and Xie, X. (2016). Adversarial deep structural networks for mammographic mass segmentation. arXiv.","DOI":"10.1101\/095786"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Miksik, O., Vineet, V., Lidegaard, M., Prasaath, R., Nie\u00dfner, M., Golodetz, S., Hicks, S.L., P\u00e9rez, P., Izadi, S., and Torr, P.H. (2015, January 18\u201323). The semantic paintbrush: Interactive 3d mapping and recognition in large outdoor spaces. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Seoul, Korea.","DOI":"10.1145\/2702123.2702222"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Siam, M., Elkerdawy, S., Jagersand, M., and Yogamani, S. (2017, January 16\u201319). Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges. Proceedings of the IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), Yokohama, Japan.","DOI":"10.1109\/ITSC.2017.8317714"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"1915","DOI":"10.1109\/TPAMI.2012.231","article-title":"Learning hierarchical features for scene labeling","volume":"35","author":"Farabet","year":"2013","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_72","unstructured":"Farabet, C., Couprie, C., Najman, L., and Lecun, Y. (July, January 26). Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers. Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland."},{"key":"ref_73","unstructured":"Grangier, D., Bottou, L., and Collobert, R. (2009, January 14\u201318). Deep convolutional networks for scene parsing. Proceedings of the ICML 2009 Deep Learning Workshop, Montreal, QC, Canada."},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., and Darrell, T. (2015, January 7\u201312). Fully convolutional networks for semantic segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Noh, H., Hong, S., and Han, B. (2015, January 7\u201313). Learning deconvolution network for semantic segmentation. Proceedings of the IEEE International Conference on Computer Vision, Santiago, Chile.","DOI":"10.1109\/ICCV.2015.178"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_77","unstructured":"Yu, F., and Koltun, V. (2016, January 2\u20134). Multi-scale context aggregation by dilated convolutions. Proceedings of the International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Yang, Y., Wang, J., Xu, W., and Yuille, A.L. (2016, January 27\u201330). Attention to scale: Scale-aware semantic image segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.396"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Qi, G.J. (2016, January 27\u201330). Hierarchically Gated Deep Networks for Semantic Segmentation. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.249"},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., and Brox, T. (2015, January 5\u20139). U-net: Convolutional networks for biomedical image segmentation. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Munich, Germany.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., and Brox, T. (2017, January 21\u201326). Flownet 2.0: Evolution of optical flow estimation with deep networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.179"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Ummenhofer, B., Zhou, H., Uhrig, J., Mayer, N., Ilg, E., Dosovitskiy, A., and Brox, T. (2017, January 21\u201326). DeMoN: Depth and motion network for learning monocular stereo. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.596"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"6022","DOI":"10.1109\/TIP.2019.2924172","article-title":"DeepCorrect: Correcting DNN models against image distortions","volume":"28","author":"Borkar","year":"2019","journal-title":"IEEE Trans. Image Process."},{"key":"ref_84","doi-asserted-by":"crossref","unstructured":"Dodge, S.F., and Karam, L.J. (2016, January 6\u20138). Understanding How Image Quality Affects Deep Neural Networks. Proceedings of the International Conference on Quality of Multimedia Experience (QoMEX), Lisbon, Portugal.","DOI":"10.1109\/QoMEX.2016.7498955"},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Winterlich, A., Hughes, C., Kilmartin, L., Glavin, M., and Jones, E. (2015). An oriented gradient based image quality metric for pedestrian detection performance evaluation. Signal Process. Image Commun., 31.","DOI":"10.1016\/j.image.2014.12.001"},{"key":"ref_86","unstructured":"Yahiaoui, L., Horgan, J., Yogamani, S., Hughes, C., and Deegan, B. (2018, January 29\u201331). Impact Analysis and Tuning Strategies for Camera Image Signal Processing Parameters in Computer Vision. Proceedings of the Irish Machine Vision and Image Processing Conference (IMVIP), Belfast, UK."},{"key":"ref_87","first-page":"269","article-title":"A Comparison of various Edge Detection Techniques used in Image Processing","volume":"9","author":"Shrivakshan","year":"2012","journal-title":"Int. J. Comput. Sci. Issues"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1109\/TPAMI.1987.4767941","article-title":"Image Analysis Using Mathematical Morphology","volume":"PAMI-9","author":"Haralick","year":"1987","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.cviu.2007.09.014","article-title":"Speeded-Up Robust Features (SURF)","volume":"110","author":"Bay","year":"2008","journal-title":"Comput. Vis. Image Underst."},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Rublee, E., Rabaud, V., Konolige, K., and Bradski, G. (2011, January 6\u201313). ORB: An efficient alternative to SIFT or SURF. Proceedings of the International Conference on Computer Vision (ICCV), Barcelona, Spain.","DOI":"10.1109\/ICCV.2011.6126544"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Alcantarilla, P.F., Bartoli, A., and Davison, A.J. (2012, January 7\u201313). KAZE Features. Proceedings of the European Conference on Computer Vision (ECCV), Florence, Italy.","DOI":"10.1007\/978-3-642-33783-3_16"},{"key":"ref_92","first-page":"1281","article-title":"Fast Explicit Diffusion for Accelerated Features in Nonlinear Scale Spaces","volume":"34","author":"Alcantarilla","year":"2013","journal-title":"IEEE Trans. Patt. Anal. Mach. Intell."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/S0734-189X(87)80186-X","article-title":"Adaptive histogram equalization and its variations","volume":"39","author":"Pizer","year":"1987","journal-title":"Comput. Vision Graph. Image Process."},{"key":"ref_94","unstructured":"Diamond, S., Sitzmann, V., Boyd, S., Wetzstein, G., and Heide, F. (2017). Dirty pixels: Optimizing image classification architectures for raw sensor data. arXiv."},{"key":"ref_95","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_96","unstructured":"Snoek, J., Larochelle, H., and Adams, R.P. (2012, January 3\u20136). Practical bayesian optimization of machine learning algorithms. Proceedings of the 25th International Conference on Neural Information Processing Systems (NIPS), Lake Tahoe, NV, USA."},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Blau, Y., and Michaeli, T. (2018, January 18\u201323). The Perception-Distortion Tradeoff. Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00652"}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/5\/10\/78\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T13:23:46Z","timestamp":1760189026000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/5\/10\/78"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,9,24]]},"references-count":97,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2019,10]]}},"alternative-id":["jimaging5100078"],"URL":"https:\/\/doi.org\/10.3390\/jimaging5100078","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,9,24]]}}}