In this paper, a technique for image enhancement based on proposed edge boosting algorithm to reconstruct high quality image from a single low resolution image is described. The difficulty in single-image super-resolution is that the generic image priors resided in the low resolution input image may not be sufficient to generate the effective solutions. In order to achieve a success in super-resolution reconstruction, efficient prior knowledge should be estimated. The statistics of gradient priors in terms of priority map based on separable gradient estimation, maximum likelihood edge estimation, and local variance are introduced. The proposed edge boosting algorithm takes advantages of these gradient statistics to select the appropriate enhancement weights. The larger weights are applied to the higher frequency details while the low frequency details are smoothed. From the experimental results, the significant performance improvement quantitatively and perceptually is illustrated. It can be seen that the proposed edge boosting algorithm demonstrates high quality results with fewer artifacts, sharper edges, superior texture areas, and finer detail with low noise.
Embedded block coding with optimized truncation (EBCOT) is a key algorithm in JPEG 2000 image compression
system. Recently, the bit-plane coder architectures are capable of producing symbols at a higher rate than the capability
of the existing MQ arithmetic coders. To solve this problem, a design of a multiple-symbol processor for statistical MQ
coder architecture on FPGA is proposed. The proposed architecture takes advantage of simplicity of single-symbol
architecture while integrates several techniques in order to increase the coding rate (more than one symbol per clock),
reduce critical path, thus accelerate the coding speed. The repeated symbol statistics has been analyzed prior to the
proposed architecture using lookahead technique. This allows the proposed architecture to support encoding rate of
maximum 8 symbols per clock cycle without stalls and without excessively increasing the hardware cost. This helps to
accelerate encoding process, which leads to greatly increase throughput. From the experiments, for lossy wavelet
transform, the proposed architecture offers high throughput of at least 233.07 MCxD/S with effectively reducing the
number of clock cycles more than 35.51%.
In this paper, an adaptive edge enhancement algorithm is proposed to reconstruct a super resolution image from a
single low resolution one. In order to improve the results of the high resolution reconstruction, edge statistics is learned
from the scenes using a statistical analysis of the maximum likelihood estimation to approximate edge boosting weight
that helps to significantly enhance edge information in the high frequency area. The edge sketch image will be adaptively
combined with the results of wiener filter according to the values of the local variance. The experimental results on
several test images show the success in reconstructing the super resolution both quantitatively and perceptually.