#Image quality with copyimage driver#
#Image quality with copyimage software#

The exploration uses a cumulative-write cache exhibiting 1.5 to 2 speedup compared to the best FPGA implementations. The dimensions of this cache are explored according to the locality of the function implemented. In this paper, we target FPGA internal memories optimization by adding a small register-based multi-ported cache memory in front of each internal FPGA memory block to increase their bandwidth. Holistic design-space exploration is necessary to leverage the inherent locality of applications and reduce memory accesses. Memory optimization cannot be done solely at the application level. Internal memories on reconfigurable devices such as FPGA are limited in size, number and bandwidth. Safety imaging system using image processing requires a lot of memory whose underutilization can lead to decrease the real time performances.

#Image quality with copyimage driver#

Most of advanced driver assistance systems are developed for safety and better driving.

#Image quality with copyimage software#

Consequently, the algorithm can be used for both hardware and software solutions to video image. After comparing with some other algorithms and testing the practical performance of the algorithm in hardware device it turns out that the algorithm has clear advantages over the other algorithms in terms of quality and the relatively low time complexity which is slightly higher than the time complexity of the bilinear. The algorithm could magnify a gray image to any times within limits with acceptable result. Finally, the non-edge pixels and the edge pixels were calculated by liner interpolation in different regions and diagonal interpolation respectively, which effectively suppress the edge blurring and edge jagging. pixels and the edge pixels of the desired image could be found. In the algorithm, an image was firstly divided into a few sub-region units and then edges of the magnified image were predicted according to the correlation of the pixels in sub-region units of the original image, from which both the non-edge. To solve the problem of edge blurring and edge jaggingIn image zooming process, a new image magnification algorithm based on edge prediction was proposed.

The proposed method can be applied in two cases: with given parameters (generated noise for artificial images) or automatically evaluated parameters (unknown noise for real images). As a result, our proposed method can be also applied to remove Gaussian or Poisson noise separately. Our method is developed with the goal to combine two famous models: ROF for removing Gaussian noise and modified ROF for removing Poisson noise. In this paper we propose method to remove noise by using total variation. The type of noise in this case can be considered as combination of Gaussian and Poisson noises.

of images, including biomedical images too. One of the limits of using digital devices to create digital images is noise. Biomedical image is one of types of digital images. The researched object of imaging science is digital image that can be created by many digital devices. Today imaging science has an important development and has many applications in different fields of life.