#Open cl after effects denoiser 3 registration

Recent image classification researches such as makeup invariant face recognition, pose invariant 3D face recognition, variance based facial image registration and recognition or impact of asymmetric left and right faces for accurate age estimation uses convolutional neural network to improve their accuracy level. Hybrid geometric image representation of BoF model depends upon circular, triangular, and rectangular region based histograms over an image. Apart from the information retrieval, Histogram based image representation of BoVW model requires proper encoding of spatial information and it was addressed by Zafar et al. Bag of Features (BoF) representation in image retrieval system lacks in spatial information, which is improved with the help of histogram of triangular regions. Content based image retrieval (CBIR) system built on color histogram and DWT extracts color and texture features, whereas CBIR based on SIFT and SURF extracts scale, rotation and illumination invariant features. Recent research on Image retrieval, representation and classification have shown lots of interest on image enhancement at their preprocessing stage. Moreover, the real-time problems like remote sensing, disaster monitoring, satellite broadcasting, and traffic monitoring use image enhancement techniques to boost the detail information present inside videos and also require their quicker execution at the same time.

Image enrichment or image high boosting is one of the key aspects in the fields of image processing and computer vision that amplifies higher frequency components in an image by keeping lower frequency components as they are. Most of the real-world images recorded by sensing devices are affected by various circumstances from which blurring is one of the factors that causes image degradation due to bad weather, poor illumination, improper focusing, or image denoisification, which needs to be enhanced. From the results, we have concluded that a vectorized Butterworth high-boost filter kernel is the suitable one to provide better results among those filters, which might be highly adaptable in time bound real-time applications using various embedded devices.Р”Р„n image in the spatial domain is the pictorial description of various observations that explain thousands of meaningful information about a specific environment. Here, different image performance evaluation metrics like entropy, standard deviation, mean absolute error, percentage fit error, SSIM, correlation, and peak signal to noise ratio are applied to measure rightness of the above high-boost filters. At last, performance analysis is carried out for these two types of OpenCL kernel implementations to determine their effectiveness with regard to time consumption and accuracy. In addition to that, these kernels are modified using image vectorization technique to optimize their time utilization by reducing the execution time of these OpenCL kernels to half.

In this article, we have considered mostly used Ideal, Gaussian, Butterworth, and Laplacian of Gaussian frequency domain high-boost filters and implemented channelized OpenCL kernels for their rapid execution. OpenCL is a widely adapted parallel programming framework that provides core level data parallelism, and dedicated for heterogeneous parallel devices like from low cost DSP to high-end CPU, GPU and FPGA. Image high boost filtering uses high-boost filters to enhance the quality of an image, which has also seen in remote sensing, satellite broadcasting, classroom monitoring, and many more real-time video processing applications and requires its faster implementation.

Open cl after effects denoiser 3