Manoj Kumar

Work place: Department of Computer Science, Babasaheb Bhimrao Ambedkar University, Lucknow, India



Research Interests: Medical Image Computing, Image Processing, Image Manipulation, Image Compression, 2D Computer Graphics, Computer Graphics and Visualization, Computer Vision


Manoj Kumar, received the Masters (M.C.A.) degree in computer science in 2003 from Jawaharlal Nehru University (JNU), New Delhi, India and the Ph.D. in the field of computer vision and image processing in 2011 from Indian Institute of    Technology (IIT) Roorkee, India. Since 2011, he is working as Assistant Professor in the department of computer science, Babasaheb Bhimrao Ambedkar University, Lucknow India.  His research interest includes Computer Graphics, Vision, & Image processing, Medical imaging, Image Compression, Security, and Watermarking.. He has published various research papers in national and international journals & conferences.

Author Articles
Discrete Wavelet Transform and Cross Bilateral Filter based Image Fusion

By Sonam Manoj Kumar

DOI:, Pub. Date: 8 Jan. 2017

The main objective of image fusion is to obtain an enhanced image with more relevant information by integrating complimentary information from two source images. In this paper, a novel image fusion algorithm based on discrete wavelet transform (DWT) and cross bilateral filter (CBF) is proposed. In the proposed framework, source images are decomposed into low and high frequency subbands using DWT. The low frequency subbands of the transformed images are combined using pixel averaging method. Meanwhile, the high frequency subbands of the transformed images are fused with weighted average fusion rule where, the weights are computed using CBF on both the images. Finally, to reconstruct the fused image inverse DWT is performed over the fused coefficients. The proposed method has been extensively tested on several pairs of multi-focus and multisensor images. To compare the results of proposed method with different existing methods, a variety of image fusion quality metrics are employed for the qualitative measurement. The analysis of comparison results demonstrates that the proposed method exhibits better results than many other fusion methods, qualitatively as well as quantitatively.

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Lossy Compression of Color Images using Lifting Scheme and Prediction Errors

By Manoj Kumar Ankita Vaish

DOI:, Pub. Date: 8 Apr. 2016

This paper presents an effective compression technique for lossy compression of color images. After reducing the correlation among R, G and B planes using YCoCg-R transform, the Integer Wavelet Transform (IWT) is applied on each of the transformed planes independently up to a desired level. IWT decomposes the input image into an approximation and several detail subbands. Approximation subband is compressed losslessly using prediction errors and Huffman coding, while each of the detail subbands are compressed independently using an effective quantization and Huffman coding. To show the effectiveness of proposed scheme, it is compared with several existing schemes and a state of art for image compression JPEG2000 and it is observed that the proposed scheme outperforms over the existing techniques and JPEG2000 with less degradation in the quality of reconstructed images while achieving high compression performance.

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A New Locally Adaptive Patch Variation Based CT Image Denoising

By Manoj Kumar Manoj Diwakar

DOI:, Pub. Date: 8 Jan. 2016

The main aim of image denoising is to improve the visual quality in terms of edges and textures of images. In Computed Tomography (CT), images are generated with a combination of hardware, software and radiation dose. Generally, CT images are noisy due to hardware/software fault or mathematical computation error or low radiation dose. The analysis and extraction of medical relevant information from noisy CT images are challenging tasks for diagnosing problems. This paper presents a novel edge preserving image denoising technique based on wavelet transform.
The proposed scheme is divided into two phases. In first phase, input CT image is separately denoised using different patch size where denoising is performed based on thresholding and its method noise thresholding. The outcome of first phase provides more than one denoised images. In second phase, block wise variation based aggregation is performed in wavelet domain.
The final outcomes of proposed scheme are excellent in terms of noise suppression and structure preservation. The proposed scheme is compared with existing methods and it is observed that performance of proposed method is superior to existing methods in terms of visual quality, PSNR and Image Quality Index (IQI).

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