ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Optimization Assisted Autoregressive Method With Deep Convolutional Neural Network-Based Entropy Filter for Image Demosaicing


Affiliations
1 Department of Computer Science, Nesamony Memorial Christian College, India., India
     

   Subscribe/Renew Journal


The natural scenes are captured by digital cameras that adopt a single charged-coupled device (CCD) or Complementary Metal Oxide Semiconductor (CMOS) sensor with a Color Filter Array (CFA). To reconstruct a full color image from the mosaiced CFA image, the missing components of color are recovered by a technique called color demosaicing. This research work introduces Deep Convolution Neural Network (DCNN)-based optimization assisted autoregressive method for image demosaicing. Here, the proposed optimization algorithm, termed Adaptive Autoregressive Water Wave Optimization algorithm (Adaptive Autoregressive-WWO), that is formed by combining Conditional autoregressive value at risk (CAViaR) model and Water Wave Optimization algorithm (WWO). Fusion process is carried out for residual images to generate the final demosaiced image based on entropy measure. Here, the output generated from DCNN is the first residual image. The LPA-ICI filter utilizes the optimization algorithm that generated second order polynomial coefficients to produce the second residual output image. Moreover, this proposed method is evaluated for its performance using metrics, such as Peak signal-to-noise ratio (PSNR) and Second Derivative like Measurement (SDME) and attained the highest PSNR values of 40.379dB and highest SDME values of 50.675dB.

Keywords

Imagedemosaicing, Local Polynomial Approximation and Intersection of Confidence Interval Filter, Entropy, Fusion Process, Deep Convolution Neural Network.
Subscription Login to verify subscription
User
Notifications
Font Size

  • W. Xing and K. Egiazarian, “Residual Swin Transformer Channel Attention Network for Image Demosaicing”, Proceedings of International Conference Image Processing, pp. 1-8, 2022.

  • C.W. Park and I.K. Eom, “Image Tampering Localization using Demosaicing Patterns and Singular Value Based Prediction Residue”, IEEE Access, Vol. 9, pp. 91921-91933, 2021.

  • M. Pistellato and A. Torsello, “Deep Demosaicing for Polarimetric Filter Array Cameras”, IEEE Transactions on Image Processing, Vol. 31, pp. 2017-2026, 2022.

  • J. Luo and J. Wang, “Image Demosaicing based on Generative Adversarial Network”, Mathematical Problems in Engineering, Vol. 45, No. 2, pp. 1-14, 2020.

  • W. Xing and K. Egiazarian, “End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution”, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, pp. 3507-3516, 2021.

  • K. Hirakawa and T.W. Parks, “Joint Demosaicing and Denoising”, IEEE Transactions on Image Processing, Vol. 15, No. 8, pp. 2146-2157, 2006.

  • L. Zhang and W. Wu, “Color Demosaicking via Directional Linear Minimum Mean Square-Error Estimation”, IEEE Transactions on Image Processing, Vol. 14, No. 12, pp. 2167-2178, 2005.

  • M. Gharbi and F. Durand, “Deep Joint Demosaicking and Denoising”, ACM Transactions on Graphics, Vol. 35, No. 6, pp. 1-12, 2016.

  • R. Tan and L. Zhang, “Color Image Demosaicking via Deep Residual Learning”, Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1-13, 2017.

  • J.F. Bell, D. Wellington and K. Kinch, “The Mars Science Laboratory Curiosity rover Mastcam Instruments: Preflight and In-Flight Calibration, Validation, and Data Archiving”, Earth and Space Science, Vol. 4, No. 7, pp. 1-14, 2017.

  • P. Getreuer, “Malvar-He-Cutler Linear Image Demosaicking”, Image Processing on Line, Vol. 1, pp. 83- 89, 2011.

  • C. Kwan, J.F. Bell and H. Kerner, “Demosaicing Enhancement using Pixel-Level Fusion”, Signal, Image and Video Processing, Vol. 12, No. 4, pp. 749-756, 2018.

  • O.A. Elgendy and J. Ma, “Low-Light Demosaicking and Denoising for Small Pixels using Learned Frequency Selection”, IEEE Transactions on Computational Imaging, Vol. 7, pp. 137-150, 2021.

  • V.N.V. Satya Prakash and T. Jaya Chandra Prasad, “Color Image Demosaicing using Sparse Based Radial Basis Function Network”, Alexandria Engineering Journal, Vol. 56, No. 4, pp. 477-483, 2017.

  • W. Ye, “Color Image Demosaicing using Iterative Residual Interpolation”, IEEE Transactions on Image Processing, Vol. 24, No. 12, pp. 5879-5891, 2015.

  • K. Zhang and L. Zhang, “Learning Deep CNN Denoiser Prior for Image Restoration”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3929-3938, 2017.

  • D. Paliy and K. Egiazarian, “Spatially Adaptive Color Filter Array Interpolation for Noiseless and Noisy Data”, International Journal of Imaging Systems and Technology, Vol. 17, No. 3, pp. 105-122, 2007.

  • R.F. Engle and S. Manganelli, “CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles”, Journal of Business and Economic Statistics, Vol. 22, No. 4, pp. 367-381, 2004.

  • Y.J. Zheng, “Water Wave Optimization: A New NatureInspired Metaheuristic”, Computers and Operations Research, Vol. 55, pp. 1-11, 2015.

  • Multispectral Image Database: Stuff, Available at https://www.cs.columbia.edu/CAVE/databases/multispectr al/stuff/, Accessed in 2022.


Abstract Views: 122

PDF Views: 0




  • Optimization Assisted Autoregressive Method With Deep Convolutional Neural Network-Based Entropy Filter for Image Demosaicing

Abstract Views: 122  |  PDF Views: 0

Authors

C. Anitha Mary
Department of Computer Science, Nesamony Memorial Christian College, India., India
A. Boyed Wesley
Department of Computer Science, Nesamony Memorial Christian College, India., India

Abstract


The natural scenes are captured by digital cameras that adopt a single charged-coupled device (CCD) or Complementary Metal Oxide Semiconductor (CMOS) sensor with a Color Filter Array (CFA). To reconstruct a full color image from the mosaiced CFA image, the missing components of color are recovered by a technique called color demosaicing. This research work introduces Deep Convolution Neural Network (DCNN)-based optimization assisted autoregressive method for image demosaicing. Here, the proposed optimization algorithm, termed Adaptive Autoregressive Water Wave Optimization algorithm (Adaptive Autoregressive-WWO), that is formed by combining Conditional autoregressive value at risk (CAViaR) model and Water Wave Optimization algorithm (WWO). Fusion process is carried out for residual images to generate the final demosaiced image based on entropy measure. Here, the output generated from DCNN is the first residual image. The LPA-ICI filter utilizes the optimization algorithm that generated second order polynomial coefficients to produce the second residual output image. Moreover, this proposed method is evaluated for its performance using metrics, such as Peak signal-to-noise ratio (PSNR) and Second Derivative like Measurement (SDME) and attained the highest PSNR values of 40.379dB and highest SDME values of 50.675dB.

Keywords


Imagedemosaicing, Local Polynomial Approximation and Intersection of Confidence Interval Filter, Entropy, Fusion Process, Deep Convolution Neural Network.

References