Neural Edge Enhancer for Supervised Edge Enhancement from Noisy Images

We propose a new edge enhancer based on a modified multilayer neural network, which is called a neural edge enhancer (NEE), for enhancing the desired edges clearly from noisy images. The NEE is a supervised edge enhancer: Through training with a set of input noisy images and teaching edges, the NEE acquires the function of a desired edge enhancer. The input images are synthesized from noiseless images by addition of noise. The teaching edges are made from the noiseless images by performing the desired edge enhancer. To investigate the performance, we carried out experiments to enhance edges from noisy artificial and natural images. By comparison with conventional edge enhancers, the following was demonstrated: The NEE was robust against noise, was able to enhance continuous edges from noisy images, and was superior to the conventional edge enhancers in similarity to the desired edges. To gain insight into the nonlinear kernel of the NEE, we performed analyses on the trained NEE. The results suggested that the trained NEE acquired directional gradient operators with smoothing. Furthermore, we propose a method for edge localization for the NEE. We compared the NEE, together with the proposed edge localization method, with a leading edge detector. The NEE was proven to be useful for enhancing edges from noisy images.

[1]  I.E. Abdou,et al.  Quantitative design and evaluation of enhancement/thresholding edge detectors , 1979, Proceedings of the IEEE.

[2]  Manfred H. Hueckel.  An Operator Which Locates Edges in Digitized Pictures , 1971, J. ACM.

[3]  Joos Vandewalle,et al.  Precise edge detection: representation by Boolean functions, implementation on the CNN , 1998, 1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359).

[4]  Joos Vandewalle,et al.  Multi-Valued and Universal Binary Neurons: Theory, Learning and Applications , 2012 .

[5]  Koji Tochinai,et al.  An edge detection by using self-organization , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[6]  Aggelos K. Katsaggelos,et al.  Noise reduction filters for dynamic image sequences: a review , 1995, Proc. IEEE.

[7]  Kenji Suzuki,et al.  A Simple Neural Network Pruning Algorithm with Application to Filter Synthesis , 2001, Neural Processing Letters.

[8]  Jaakko Astola,et al.  Adaptive multistage weighted order statistic filters based on the backpropagation algorithm , 1994, IEEE Trans. Signal Process..

[9]  John C. Russ,et al.  The Image Processing Handbook , 2016, Microscopy and Microanalysis.

[10]  Cüneyt Güzelis,et al.  Recurrent perceptron learning algorithm for CNNs with application to edge detection , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  Kenji Suzuki,et al.  Computer-aided diagnosis system for coronary artery stenosis using a neural network , 2001, SPIE Medical Imaging.

[12]  Tamás Roska,et al.  CNN‐based difference‐controlled adaptive non‐linear image filters , 1998 .

[13]  Charles A. Bouman,et al.  Dual stack filters and the modified difference of estimates approach to edge detection , 1997, IEEE Trans. Image Process..

[14]  G. S. Robinson.  Edge detection by compass gradient masks , 1977 .

[15]  W. Lunscher,et al.  Optimal Edge Detector Design I: Parameter Selection and Noise Effects , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Hideo Fujimoto,et al.  Edge detection by neural network with line process , 1993, Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan).

[17]  E. Dubois,et al.  Digital picture processing , 1985, Proceedings of the IEEE.

[18]  Riccardo Poli,et al.  Recovery of the 3-D shape of the left ventricle from echocardiographic images , 1995, IEEE Trans. Medical Imaging.

[19]  Isao Horiba,et al.  Efficient approximation of a neural filter for quantum noise removal in X-ray images , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[20]  Joos Vandewalle,et al.  Multi-Valued and Universal Binary Neurons , 2000 .

[21]  Demetri Terzopoulos,et al.  Snakes: Active contour models , 2004, International Journal of Computer Vision.

[22]  Kaoru Arakawa,et al.  A nonlinear digital filter using multi-layered neural networks , 1990, IEEE International Conference on Communications, Including Supercomm Technical Sessions.

[23]  Ling Guan,et al.  Neural vision system and applications in image processing and analysis , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[24]  Kenji Suzuki,et al.  Linear-time connected-component labeling based on sequential local operations , 2003, Comput. Vis. Image Underst..

[25]  S. Armato,et al.  Massive training artificial neural network (MTANN) for reduction of false positives in computerized detection of lung nodules in low-dose computed tomography. , 2003, Medical physics.

[26]  Suchendra M. Bhandarkar,et al.  An edge detection technique using genetic algorithm-based optimization , 1994, Pattern Recognit..

[27]  Jun Shen,et al.  Artificial neural networks for boundary extraction , 1996, 1996 IEEE International Conference on Systems, Man and Cybernetics. Information Intelligence and Systems (Cat. No.96CH35929).

[28]  V.M. Brea,et al.  Image segmentation based on active contours using discrete time cellular neural networks , 1998, 1998 Fifth IEEE International Workshop on Cellular Neural Networks and their Applications. Proceedings (Cat. No.98TH8359).

[29]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  D. Hubel,et al.  Receptive fields, binocular interaction and functional architecture in the cat's visual cortex , 1962, The Journal of physiology.

[31]  Wolfram H. H. J. Lunscher,et al.  The Asymptotic Optimal Frequency Domain Filter for Edge Detection , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Fred M. Dickey,et al.  An Optimal Frequency Domain Filter for Edge Detection in Digital Pictures , 1979, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[33]  Csaba Rekeczky,et al.  Image segmentation and edge detection via constrained diffusion and adaptive morphology: a CNN approach to bubble/debris image enhancement , 1997 .

[34]  Hiroshi Nagahashi,et al.  Boundary Detection of Color Images Using Neural Networks. , 1997 .

[35]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[36]  Tomaso A. Poggio,et al.  On Edge Detection , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Nirwan Ansari,et al.  Structure and properties of generalized adaptive neural filters for signal enhancement , 1996, IEEE Trans. Neural Networks.

[38]  Geoffrey E. Hinton,et al.  Learning representations of back-propagation errors , 1986 .

[39]  Gosuke Ohashi,et al.  Boundary estimation method for ultrasonic 3D imaging , 1993 .

[40]  Tamás Roska,et al.  CNN-based difference-controlled adaptive non-linear image filters , 1998, Int. J. Circuit Theory Appl..

[41]  Jaakko Astola,et al.  A new class of nonlinear filters-neural filters , 1993, IEEE Trans. Signal Process..

[42]  Werner Frei,et al.  Fast Boundary Detection: A Generalization and a New Algorithm , 1977, IEEE Transactions on Computers.

[43]  Ken-ichi Funahashi,et al.  On the approximate realization of continuous mappings by neural networks , 1989, Neural Networks.

[44]  Andrew R. Barron,et al.  Universal approximation bounds for superpositions of a sigmoidal function , 1993, IEEE Trans. Inf. Theory.

[45]  Manfred H. Hueckel.  A Local Visual Operator Which Recognizes Edges and Lines , 1973, JACM.

[46]  Igor N. Aizenberg,et al.  Processing of noisy and small-detailed gray-scale images using cellular neural networks , 1997, J. Electronic Imaging.

[47]  Steven W. Zucker,et al.  Local Scale Control for Edge Detection and Blur Estimation , 1996, IEEE Trans. Pattern Anal. Mach. Intell..

[48]  Takao Hinamoto,et al.  Image restoration by Hopfield networks considering the line process , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[49]  Pekka J. Toivanen,et al.  Edge Detection of Multispectral Images Using the 1-D Self-Organizing Map , 1998 .

[50]  Josef Kittler,et al.  Edge-Labeling Using Dictionary-Based Relaxation , 1990, IEEE Trans. Pattern Anal. Mach. Intell..

[51]  S. Pizer,et al.  The Image Processing Handbook , 1994 .

[52]  Kenji Suzuki,et al.  An Approach to Synthesize Filters with Reduced Structures Using a Neural Network , 2000 .

[53]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[54]  Josef Kittler,et al.  Optimal Edge Detectors for Ramp Edges , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[55]  Kenji Suzuki,et al.  Designing the optimal structure of a neural filter , 1998, Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No.98TH8378).

[56]  J.H. Elder,et al.  Scale space localization, blur, and contour-based image coding , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[57]  Z. Vassy,et al.  A Simplified And Fast Version Of The Huockel Operator For Finding Optimal Edges in Pictures , 1975, IJCAI.

[58]  Ken-ichi Suzuki,et al.  Neural Filter with Selection of Input Features and Its Application to Image Quality Improvement of Medical Image Sequences , 2002 .

[59]  Jun Shen,et al.  An optimal linear operator for step edge detection , 1992, CVGIP Graph. Model. Image Process..

[60]  Jim R. Parker,et al.  Algorithms for image processing and computer vision , 1996 .

[61]  Constantine Butakoff,et al.  Image processing using cellular neural networks based on multi-valued and universal binary neurons , 2002, J. VLSI Signal Process..

[62]  Kenji Suzuki,et al.  Efficient approximation of neural filters for removing quantum noise from images , 2002, IEEE Trans. Signal Process..

[63]  Tony Lindeberg,et al.  Edge Detection and Ridge Detection with Automatic Scale Selection , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[64]  Kenji Suzuki,et al.  Recognition of Coronary Arterial Stenosis Using Neural Network on DSA System , 1995, Systems and Computers in Japan.

[65]  Richard O. Duda,et al.  Pattern classification and scene analysis , 1974, A Wiley-Interscience publication.

[66]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[67]  William K. Pratt,et al.  Digital image processing (2nd ed.) , 1991 .

[68]  Geoffrey E. Hinton,et al.  Learning internal representations by error propagation , 1986 .

[69]  Tony Lindeberg,et al.  Feature Detection with Automatic Scale Selection , 1998, International Journal of Computer Vision.

[70]  Heekuck Oh,et al.  Neural Networks for Pattern Recognition , 1993, Adv. Comput..

[71]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[72]  M. Y. Siyal,et al.  Edge detection with BP neural networks , 1998, ICSP '98. 1998 Fourth International Conference on Signal Processing (Cat. No.98TH8344).

[73]  G. F. Cooper,et al.  Development of the Brain depends on the Visual Environment , 1970, Nature.

[74]  Kenji Suzuki,et al.  Training under achievement quotient criterion , 2000, Neural Networks for Signal Processing X. Proceedings of the 2000 IEEE Signal Processing Society Workshop (Cat. No.00TH8501).