You might also look for MatLab based image processing projects for more advanced digital image processing.
The edge detection is one of the key techniques in most image processing applications. The canny edge detection is proven to be able to significantly outperform existing edge detection techniques due to its superior performance. Unfortunately, the implementation of the systems in real-time is computationally complex, high hardware cost with increased latency.
The proposed canny edge detection algorithm uses approximation methods to replace the complex operations; the pipelining is employed to reduce the latency. Finally, this algorithm is implemented on Xilinx Virtex-5 FPGA. When compared with the previous hardware architecture for canny edge detection, the proposed architecture requires fewer hardware costs and takes 1ms to detect the edges of 512x512 image.
In this paper, a low-complexity color interpolation algorithm is proposed for the very-large-scale integration (VLSI) implementation in real-time applications. The proposed novel algorithm consists of an edge detector, an anisotropic weighting model, and a filter-based compensator. The anisotropic weighting model is designed to catch more information in horizontal than vertical directions.
The filter-based compensation methodology includes a Laplacian and spatial sharpening filters, which are developed to improve the edge information and reduce the blurring effect. In addition, the hardware cost was successfully reduced by hardware sharing and reconfigurable design techniques.
The VLSI architecture of the proposed design achieves 200 MHz with 5.2-K gate counts, and its core area is 64 236 μm2 synthesized by a 0.18-μm CMOS process. Compared with the previous low-complexity techniques, this paper not only reduces gate counts or power consumption by more than 8% or 91.7%, respectively, but also improves the average color peak signal-to -noise ratio quality by more than 1.6 dB.
Edge detection is one of the most important stages in image processing. The Canny edge detection algorithm is most widely used edge detection algorithm because of it advantages. In this paper we present canny edge detection algorithm implemented on Spartan 3E FPGA and developed VGA interfacing for displaying images on the screen. In this paper we have taken 128×128 Image and displayed same on the monitor through FPGA.
Image and video signals might be corrupted by impulse noise in the process of signal acquisition and transmission. In this paper, an efficient VLSI implementation for removing impulse noise is presented. Our extensive experimental results show that the proposed technique preserves the edge features and obtains excellent performances in terms of quantitative evaluation and visual quality.
The design requires only low computational complexity and two line memory buffers. Its hardware cost is quite low. Compared with previous VLSI implementations, our design achieves better image quality with less hardware cost. Synthesis results show that the proposed design yields a processing rate of about 167 M samples/second by using TSMC 0.18 m technology.
High performance video standards use prediction techniques to achieve high picture quality at low bit rates. The type of prediction decides the bit rates and the image quality. Intra Prediction achieves high video quality with significant reduction in bit rate. This paper presents novel area optimized architecture for Intra prediction of H.264 decoding at HDTV resolution.
The architecture has been validated on a Xilinx Virtex-5 FPGA based platform and achieved a frame rate of 64 fps. The architecture is based on multi-level memory hierarchy to reduce latency and ensure optimum resources utilization. It removes redundancy by reusing same functional blocks across different modes. The proposed architecture uses only 13% of the total LUTs available on the Xilinx FPGA XC5VLX50T.