Optimized Dynamic Stochastic Resonance framework for enhancement of structural details of satellite images

Abstract Image enhancement is an essential tool for increasing the contrast of an image to visualize the dark and bright areas. The enhancement algorithms are very much relevant in remote sensing applications as the satellite images are normally of poor contrast. The dynamic stochastic resonance (DSR) attains the enhancement of poor contrast and low illuminated images by utilizing the internal noise. The conventional DSR method employed for enhancing the dark images demands proper tuning of bistable element parameters and appropriate transform domain which are found to be challenging. In this paper, we propose chaotic grey wolf optimizer to attain the optimized parameters of dynamic stochastic resonance in non-sub sampled shearlet transform domain (NSST) to enhance the low contrast satellite images. In addition, we have tested the proposed method on a variety of satellite images captured by different sensors of local cities and global areas. The quality of the proposed method is compared with that of recent enhancement algorithms. The proposed method demonstrates to be the most reliable in enhancing the image structure contrast while preserving the true colors of satellite images. The source code and dataset is available in https://github.com/shyamfec/ODSRF .

[1]  Neeraj Sharma,et al.  An Optimized Cascaded Stochastic Resonance for the Enhancement of Brain MRI , 2018, IRBM.

[2]  D. Jude Hemanth,et al.  Bat Algorithm Based Non-linear Contrast Stretching for Satellite Image Enhancement , 2020, Geosciences.

[3]  Eerapu Karuna Kumari,et al.  A robust framework for quality enhancement of aerial remote sensing images , 2018 .

[4]  Prabir Kumar Biswas,et al.  Enhancement of dark and low-contrast images using dynamic stochastic resonance , 2013, IET Image Process..

[5]  Julien Michel,et al.  An Inquiry on Contrast Enhancement Methods for Satellite Images , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[6]  S. Gabarda,et al.  Blind image quality assessment through anisotropy. , 2007, Journal of the Optical Society of America. A, Optics, image science, and vision.

[7]  C. E. SHANNON,et al.  A mathematical theory of communication , 1948, MOCO.

[8]  Jiye Wang,et al.  Remote Sensing Image Enhancement Using Regularized-Histogram Equalization and DCT , 2015, IEEE Geoscience and Remote Sensing Letters.

[9]  Mongi A. Abidi,et al.  Gray-level grouping (GLG): an automatic method for optimized image contrast Enhancement-part I: the basic method , 2006, IEEE Transactions on Image Processing.

[10]  Deep Gupta,et al.  Image quality restoration framework for contrast enhancement of satellite remote sensing images , 2018 .

[11]  D. Labate,et al.  Resolution of the wavefront set using continuous shearlets , 2006, math/0605375.

[12]  Robert A. Schowengerdt,et al.  Remote sensing, models, and methods for image processing , 1997 .

[13]  Shih-Chia Huang,et al.  Efficient Contrast Enhancement Using Adaptive Gamma Correction With Weighting Distribution , 2013, IEEE Transactions on Image Processing.

[14]  P. Hänggi,et al.  Quantum stochastic resonance in symmetric systems. , 1999, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[15]  Xiao-Ping Zhang,et al.  A Probabilistic Method for Image Enhancement With Simultaneous Illumination and Reflectance Estimation , 2015, IEEE Transactions on Image Processing.

[16]  Sos S. Agaian,et al.  Transform Coefficient Histogram-Based Image Enhancement Algorithms Using Contrast Entropy , 2007, IEEE Transactions on Image Processing.

[17]  Hossam M. Zawbaa,et al.  Impact of Chaos Functions on Modern Swarm Optimizers , 2016, PloS one.

[18]  Andrew Lewis,et al.  Grey Wolf Optimizer , 2014, Adv. Eng. Softw..

[19]  Kiyoharu Aizawa,et al.  Dark and low-contrast image enhancement using dynamic stochastic resonance in discrete cosine transform domain , 2013, APSIPA Transactions on Signal and Information Processing.

[20]  J. Lindner,et al.  Harvesting wind energy to detect weak signals using mechanical stochastic resonance. , 2016, Physical review. E.

[21]  Shilpa Suresh,et al.  A Novel Adaptive Cuckoo Search Algorithm for Contrast Enhancement of Satellite Images , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[22]  A. Sutera,et al.  The mechanism of stochastic resonance , 1981 .

[23]  Xiang Pan,et al.  A nonlinear monostable filter for bipolar pulse signal detection , 2007 .

[24]  Wen Gao,et al.  A Bio-Inspired Multi-Exposure Fusion Framework for Low-light Image Enhancement , 2017, ArXiv.

[25]  Wen-Huang Cheng,et al.  Joint Enhancement and Denoising Method via Sequential Decomposition , 2018, 2018 IEEE International Symposium on Circuits and Systems (ISCAS).

[26]  Zohair Al-Ameen,et al.  Nighttime image enhancement using a new illumination boost algorithm , 2019, IET Image Process..

[27]  Michael J. Brennan,et al.  Stochastic resonance in a nonlinear mechanical vibration isolation system , 2016 .

[28]  Albert A. Michelson,et al.  Studies in Optics , 1995 .

[29]  Carson C. Chow,et al.  Aperiodic stochastic resonance. , 1996, Physical review. E, Statistical physics, plasmas, fluids, and related interdisciplinary topics.

[30]  Shilpa Suresh,et al.  Modified differential evolution algorithm for contrast and brightness enhancement of satellite images , 2017, Appl. Soft Comput..

[31]  Ashish Verma,et al.  Optimized Multistable Stochastic Resonance for the Enhancement of Pituitary Microadenoma in MRI , 2018, IEEE Journal of Biomedical and Health Informatics.

[32]  Kurths,et al.  Doubly stochastic resonance , 2000, Physical review letters.

[33]  Qiaoqiao Li,et al.  Feature-Linking Model for Image Enhancement , 2016, Neural Computation.

[34]  Ronggang Wang,et al.  A New Low-Light Image Enhancement Algorithm Using Camera Response Model , 2017, 2017 IEEE International Conference on Computer Vision Workshops (ICCVW).