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Govind Ballabh Pant University of Agriculture and Technology, Pantnagar

After independence, development of the rural sector was considered the primary concern of the Government of India. In 1949, with the appointment of the Radhakrishnan University Education Commission, imparting of agricultural education through the setting up of rural universities became the focal point. Later, in 1954 an Indo-American team led by Dr. K.R. Damle, the Vice-President of ICAR, was constituted that arrived at the idea of establishing a Rural University on the land-grant pattern of USA. As a consequence a contract between the Government of India, the Technical Cooperation Mission and some land-grant universities of USA, was signed to promote agricultural education in the country. The US universities included the universities of Tennessee, the Ohio State University, the Kansas State University, The University of Illinois, the Pennsylvania State University and the University of Missouri. The task of assisting Uttar Pradesh in establishing an agricultural university was assigned to the University of Illinois which signed a contract in 1959 to establish an agricultural University in the State. Dean, H.W. Hannah, of the University of Illinois prepared a blueprint for a Rural University to be set up at the Tarai State Farm in the district Nainital, UP. In the initial stage the University of Illinois also offered the services of its scientists and teachers. Thus, in 1960, the first agricultural university of India, UP Agricultural University, came into being by an Act of legislation, UP Act XI-V of 1958. The Act was later amended under UP Universities Re-enactment and Amendment Act 1972 and the University was rechristened as Govind Ballabh Pant University of Agriculture and Technology keeping in view the contributions of Pt. Govind Ballabh Pant, the then Chief Minister of UP. The University was dedicated to the Nation by the first Prime Minister of India Pt Jawaharlal Nehru on 17 November 1960. The G.B. Pant University is a symbol of successful partnership between India and the United States. The establishment of this university brought about a revolution in agricultural education, research and extension. It paved the way for setting up of 31 other agricultural universities in the country.

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  • ThesisItemOpen Access
    Reconstruction of motion blurred image using wavelet transform and neural committee machine
    (G.B. Pant University of Agriculture and Technology, Pantnagar - 263145 (Uttarakhand), 2016-08) Agarwal, Saitu; Mathur, Sanjay
    Image Restoration is the process of obtaining the original image by using the knowledge of the degrading factors. Degradation comes in many forms such as blur, noise, and camera shake. One of the disadvantages of existing method is that some methods make restrictive assumptions on the Point Spread Function or the true image that limits the algorithm's portability to different applications in conventional approach; deblurring filters are applied on the degraded images without the knowledge of blur and its effectiveness. The proposed algorithm is used for the reconstruction of motion blurred images. Two factors which mainly affect an image are length of blur and angle of blur. In this thesis, concept of Wavelet decomposition and neural committee machines are applied for restoring problems in which images are degraded by blur function and corrupted by noise. In the first step of reconstruction, blurred image is decomposed using wavelet transform based on multi resolution analysis and then correlation analysis is performed to reduce the dimensionality of image pattern space and in the next step a multi layer feed forward neural network based on ensemble averaging technique is used to estimate the parameter which causes blurring. The proposed methodology uses highly non linear back propagation neuron for image restoration to get a high quality of restored image. The estimated parameters are used to calculate the value of degradation factor corresponding to blurred image and then deconvolution is performed to get the original image. Results show that the images restored using the proposed method have mean square error in the range of 0.001-.005 and peak signal to noise ratio in the range of 69dB-75dB.