Wavelet transform using Haar filter is a fast process for decomposing an image into low and high frequency sub-bands, which is an important step in multi-focus image fusion. This transformation alone is usually not sufficient to produce satisfying fused images, and therefore additional tools like focused region decision map and feature extraction are needed to enhance the fusion quality. In this paper, we propose a multi-focus image fusion algorithm that is suitable for efficient hardware implementation. The algorithm speed is significantly improved by utilizing Haar wavelet and computationally simple fusion rules. To further improve the circuit power and delay, we limit the logic operations to additions, subtractions, and multiplications only. Experiments on different benchmarks demonstrate that our proposed algorithm can reduce the fusion time by up to 99.97% compared with the state-of-the-art competing methods without compromising the fusion quality. In addition, the hardware implementation of the proposed algorithm reduces the circuit area and delay by 79.5% and 92.5%, respectively, when compared to the most competitive algorithm in this paper while maintaining similar fusion quality.

This paper presents a novel optimized quantization constraint set, acting as an add-on to existing DCT-based image restoration algorithms. The constraint set is created based on generalized Gaussian distribution which is more accurate than the commonly used uniform, Gaussian or Laplacian distributions when modeling DCT coefficients. More importantly, the proposed constraint set is optimized for individual input images and thus it is able to enhance image quality significantly in terms of signal-to-noise ratio. Experimental results indicate that the signal-to-noise ratio is improved by at least 6.78% on top of the existing state-of-the-art methods, with a corresponding expense of only 0.38% in processing time. The proposed algorithm has also been implemented in GPU, and the processing speed increases further by 20 times over that of CPU implementation. This makes the algorithm well suited for fast image retrieval in security and quality monitoring system.

This paper presents a fast and effective polarization image demosaicking algorithm, which explores inter-channel dependency of Stokes parameters for the minimization of residual aliasing artifacts after cubic spline interpolation. A guided filtering approach is used for denoising. An optimization based on the confidence level of the aforementioned guided filtering, the correlations between the demosaicked image and input, as well as the total intensity, angle and degree of linear polarization, is constructed and solved with Newton's method. Experimental results demonstrate that the proposed algorithm can surpass the existing methods in terms of both objective root mean squared error and structural similarity index by at least 36.0% and 3.4%, respectively, and by close visual inspection of the clarity of objects in the angle and degree of linear polarization images. The proposed algorithm consists of only convolutions and element-wise operations, making it fast and parallelizable for efficient GPU acceleration. An image of size 512 × 612 × 4 can be processed within 10 s on i7-6700k CPU, and gains further 5 times speedup with M4000M GPU.

Homography has wide applications in aerial photographic surveys, camera calibration, traffic scene analysis, and sports science, such as player and team performance evaluation. Unlike the mainstream homography that utilizes points as matching features, homography estimation for sports and traffic video can achieve higher accuracy and speed by utilizing straight lines in the scenes, which convey more information than points. Owing to the more stringent requirement of accuracy and computational speed for advanced video analysis, this paper presents a novel homography computational algorithm. Three major novelties are proposed and validated, which are multiple points Hough transform for straight line extraction, correspondence initialization by angle to estimate a set of quasi-optimal solutions, and the feature correspondences optimization to achieve a minimized error using genetic algorithm. With these contributions, the experiments have shown that the proposed algorithm can improve the homography computational accuracy by up to 130% and reduce the processing time by up to 96% over the state-of-the-art algorithms for the same purposes.