The aim of this paper is to develop a high resolution image from a sequence of low resolution compressed images. An image with improved resolution is desired in almost all of the applications to enhance qualitative features and is reported to be achieved by Super Resolution Image Reconstruction (SRIR). Some low resolution images of same scene which are usually rotated, translated and blurred are taken to form a super resolution image. The image registration operation orients translated, scaled and rotated images in similar way to that of source image. Lifting Wavelet Transform (LWT) with Daubechies4 coefficients is applied to color components of each image due to its less memory allocation compared to other techniques. Further Set Portioning in Hierarchical Trees (SPIHT) algorithm is applied for image compression as it possess lossless compression, fast encoding/decoding, adaptive nature. The three low resolution images are fused by spatial image fusion method. The noise component is removed by dual tree Discrete Wavelet Transform (DWT) and blur is removed by blind de-convolution or iterative blind de-convolution. Finally, the samples are interpolated to twice the number of original samples to obtain a super resolution image. The structural similarity for each intermediate image compared to source image is estimated via objective analysis and high structural similarity is observed for image constructed by the proposed method.
Image inpainting is a technique to repair damaged images or to remove/replace selected regions. It was used to repair old artwork and also a part of movie special effects. This paper presents review of many successful algorithms for image inpainting which are Texture based algorithms, Diffusion(PDE) based algorithms, Exemplar and search based algorithms and Sparsity based algorithms. Here an evaluation of two classes of algorithms: Partial Differential Equations (PDEs) based algorithms and Exemplar-Based algorithms are presented. The results show the advantages and disadvantages.Index Terms-Image inpainting, PDEs-based algorithm, Exemplar-based algorithm.
Nowadays Cheque Truncating System (CTS) compliant cheques are used in banks for saving time and reducing effort in depositing cheques. With the development of image processing it is possible to make machines read the documents instead of humans. Applying the image processing technique in the cheque deposition in banks can reduce the human effort, time and also will be cost effective. In this paper, an automatic cheque clearance system is discussed using Optical Character Recognition (OCR) system by feature extraction and pattern recognition concepts in image processing. Using this one, can reduce time, human effort and money in processing of cheques. The algorithms are executed using MATLAB.
The transpose form configuration of Finite impulse response filter (FIR) does not support for block based processing se form FIR filter architecture is optimized and implemented. The basic Data Flow Graph (DFG) of transpose form FIR filter is converted into block based DFG and retiming is inserted in the DFG for low power consumption, reduced area and minimal delay. The generalized mathematical formulation is done for the retimed block based transpose form FIR filter and it is implemented with the block size of 4 for the filter length of 16 using Verilog Hardware Description Language (HDL). Later, it is synthesized using CADENCE-RTL compiler in TSMC 45nm CMOS library and power, area and delay reports are generated. The obtained results are compared with the few existing structures.
The image viewers do not consider specific requirements and scalability for the efficient encoding and decoding for easy transmission of images in many applications. This paper presents highly scalable Set Partitioning in Hierarchical Trees (SPIHT) algorithm for Digital Imaging and Communications in Medicine(DICOM) images. This algorithm is implemented using scalable line based Discrete Wavelet Transform(DWT) encoder and decoder. The performance metrics Mean Square Error (MSE) and Peak Signal to Noise Ratio(PSNR) are considered for achieving the high quality and scalable lossless image compression. This algorithm is also compatible with many heterogeneous networks.
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