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Finding the median of a set of data within a window of finite size is computationally challenging on account of the complexity in sorting. Compared with the established nine-stage systolic arrays, an eightstage systolic array to find the median of a fixed 3 × 3 window of n bit integers is proposed. The proposed array requires a fewer number of signal paths, and is faster for fixed resource. The method employed uses selective comparators for finding the median by avoiding comparators required to obtain fully sorted list. This reduces the partial sorting to eight-stage systolic array. However, this is applicable only to median sorting of 3 × 3 fixed window. The method employs combinatorial circuit implementation; consequently there is 12% gain in speed and 7% fewer signal paths. Circuit with memory cells at every stage and eight latency cycles results in 36% speed gain over the state-of-the-art non-sorting based methods. This method provides superior performance in comparison with the methods available for fixed 3 × 3 window-based median filters.

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Objective – Real-time fundus images captured to detect multiple diseases are prone to different quality issues like illumination, noise, etc., resulting in less visibility of anomalies. So, enhancing the retinal fundus images is essential for a better prediction rate of eye diseases. Method - In this paper, we propose Lab color space-based enhancement techniques for retinal image enhancement. Existing research works don’t consider the relation between color spaces of the fundus image in selecting a specific channel to perform retinal image enhancement. Our unique contribution to this research work is utilizing the color dominance of an image in quantifying the distribution of information in the blue channel and performing enhancement in Lab space followed by a series of steps to optimize overall brightness and contrast. Results - The test set of the Retinal Fundus Multi-disease Image Dataset is used to evaluate the performance of the proposed enhancement technique in identifying the presence or absence of retinal abnormality. The proposed technique achieved an accuracy of 89.53 percent.