An Improved Morphological Antialiasing Algorithm Implemented In FPGA

Ai, Dihan; Xue, Junjie; Wang, Mingjiang

doi:10.1109/ctisc54888.2022.9849766

Cited by 1 publication

(1 citation statement)

References 4 publications

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“…About three years after the MLAA was proposed, Jorge Jimenez proposed its enhanced version: Enhanced Subpixel Morphological Anti-Aliasing (SMAA) [18]. As a sign of the booming post-processing anti-aliasing technology, the core idea of MLAA and SMAA is firstly detecting the image edges, identifying and categorizing the edges, then re-vectorizing the edges, calculating the blending factor, and blending the current pixel with the surrounding pixels to achieve smooth aliasing [19]. Although SMAA has excellent anti-aliasing effect and high implementation efficency [20], its complex calculation process requires a lot of cache space and calculation overhead.…”

Section: Introductionmentioning

confidence: 99%

Improved anti-aliasing algorithm based on fast approximate anti-aliasing

Zou,

Xu,

et al. 2024

Fifteenth International Conference on Graphics and Image Processing (ICGIP 2023)

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The Fast Approximate Anti-Aliasing algorithm is one of the most widely used real-time anti-aliasing algorithms in practice in recent years. It has superior low-cost and high-efficiency anti-aliasing processing advantages as a postprocessing algorithm. This algorithm directly achieves a real-time anti-aliasing effect on the image vision by blending and smoothing the object edge pixels in the image using blending factors. However, in practice, because the fast approximate anti-aliasing algorithm is based on the pixel blending principle to achieve the visual effect of anti-aliasing, it has the problem of blurring the image screen. To solve this problem, this paper proposes an improved anti-aliasing algorithm based on the fast approximate anti-aliasing algorithm, combining the Laplacian sharpening variant. The general optimization idea of this improved algorithm is to improve the blending factor formula and pixel blending method of the original fast anti-aliasing algorithm and to further sharpen the blended anti-aliasing results using a sharpening kernel. In this way, this algorithm can achieve improvement in the sharpness of the anti-aliasing result image while ensuring the superior real-time performance of it as a post-processing method. The experimental results show that the generating image of this improved anti-aliasing algorithm is sharper compared with the original Fast Approximate Anti-Aliasing algorithm.

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Section: Introductionmentioning

confidence: 99%