Vector median filters efficiently reduce noise while preserving image details. However, their high computational complexity for color images makes them impractical for real-time systems. We propose new computationally efficient filtering algorithms, called index mapping filters (IMF). These filtering algorithms are accelerated by implementing them on a massively data parallel processor array. In addition to greater computational efficiency, these algorithms result in robust noise reduction of corrupted color images. Analyses of mean square error, signal-to-noise-ratio, and visual comparison metrics indicate that IMF are competitive with the vector median filter (VMF) in their ability to correct impulse noise in color images. These algorithms are implemented on a SIMD processor array being developed for high efficiency, high-performance portable products. Executing on a 4096 node SIMD chip operating at 50 MHz, IMF 3/spl times/3 window applied to a 256/spl times/256 color image would take 442 microseconds (22104 clock cycles) for index mapping distance filter (IMDF) and 408 microseconds (20415 clock cycles) for index mapping median filter (IMMF).