LettersRatio rule and homomorphic filter for enhancement of digital colour image
Introduction
High dynamic range is common in scenes that include both dark shadows and bright light sources. These scenes are difficult to represent because their dynamic range greatly exceeds the range of most electronic capturing devices. As a result, the dynamic range of an electronic image is compressed, obscuring actual scenic details and/or colours. Image enhancement is a process to improve the appearance of an electronic image as perceived by humans [7], [8] or to render the image more suitable for machine analysis [12]. The majority of enhancement algorithms have focused on the improvement of grey-level images [2], [4]. But, direct extensions of these processes into the colour domain have resulted in degraded or suboptimal images. Although it is possible to enhance a digital colour image by applying existing grey-level image enhancement algorithms to each red, green, and blue (RGB) component [2], [3], [7], [8], [13], the resulting image may not be enhanced optimally. Image distortion will result if the RGB components are not properly recombined after the grey-level enhancement algorithms are applied to each component.
In this letter, we propose a new neural network based technique to provide simultaneous improvement of the dynamic range and colour rendition of digital colour images. The method is based on a homomorphic processing system [3], [13] and a novel Ratio rule learning algorithm [10], [11]. The Ratio rule forms a line in the state space using all the stable points representing patterns of a family. All instances of this family with variations due to extraneous influence would converge to the line of attraction. The colour image enhancement process with a description of the line attractor is presented in Section 2. Section 3 provides the test results and discussions of the enhancement process and Section 4 presents the conclusions.
Section snippets
Colour image enhancement model
The human visual system can detect the range of light spectrum with wavelengths from 380 to 780 nm. Our perception of a colour is determined by which combination of R, G, and B sensors are excited and by how much they are excited [1]. Fig. 1 shows the spectral sensitivity of a typical human visual system. All digital colour devices that handle the storage and reproduction of colour images do the same by storing RGB values. Digitally storing an image requires that it first be broken down into a
Results and discussions
Several experiments have been conducted to validate the performance of the proposed method. The criterion for evaluation of the enhancement algorithm is judged by the dynamic range compression and the colour rendition of the image. The proposed method has been used to perform digital colour image enhancement in more than 100 images. These images are selected specifically for testing the dynamic range compression and colour restoration properties of the proposed method. Fig. 3 shows a typical
Conclusions
A new technique of colour image enhancement using a recurrent neural network in conjunction with a homomorphic filter is presented in this paper. The proposed method provides simultaneous improvement of the dynamic range and colour rendition to digital colour images. Moreover, it gives a new framework for applying any grey-level enhancement algorithm in the colour domain. This paper also demonstrates the use of an associative memory that encapsulates instances of colours for digital colour
Ming-Jung Seow was born in Kuala Lumpur, Malaysia, in 1979. He received his B.S. and M.S. degrees in Computer Engineering from the Old Dominion University, Virginia, USA, in 2001 and 2002, respectively. He is currently pursuing his doctoral degree in the Department of Electrical and Computer Engineering at the Old Dominion University. His research interests are in the areas of recurrent neural network architectures and learning algorithms, and applications of neural networks and machine
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2022, Information FusionCitation Excerpt :From such a perspective, the haze-degraded images can be enhanced by boosting the high-frequency component and reducing the low-frequency component. The commonly adopted approaches mainly include homomorphic filtering methods [46,47], wavelet transform methods [48,49] and curvelet transform methods [50]. Among those methods, the wavelet transform is more popular due to its efficiency and better ability to express the image in multi-resolution [169].
Ming-Jung Seow was born in Kuala Lumpur, Malaysia, in 1979. He received his B.S. and M.S. degrees in Computer Engineering from the Old Dominion University, Virginia, USA, in 2001 and 2002, respectively. He is currently pursuing his doctoral degree in the Department of Electrical and Computer Engineering at the Old Dominion University. His research interests are in the areas of recurrent neural network architectures and learning algorithms, and applications of neural networks and machine learning techniques in image processing, computer vision and pattern recognition. He published over 30 technical papers in journals and conference proceedings.
Vijayan K. Asari joined the Department of Electrical and Computer Engineering, Old Dominion University, Virginia as an Associate Professor in August 2000. Dr. Asari received the B.Sc. (Engg.) degree in Electronics and Communication Engineering from the University of Kerala, M.Tech. and Ph.D. degrees in Electrical Engineering from the Indian Institute of Technology, Madras, India. He has been working as an Assistant Professor at the T.K.M. College of Engineering (University of Kerala), India. He was with the National University of Singapore from 1996 to 1998 and with the School of Computer Engineering at Nanyang Technological University, Singapore from 1998 to 2000. Dr. Asari is the Director of the Computational Intelligence and Machine Vision Laboratory at ODU. His research activities are in the areas of neural networks, image processing, computer vision, and digital system architectures for application specific integrated circuits. Dr. Asari is a senior member of the IEEE.