Improving Image Resolution with Edge-Targeted Filter Evolution

Government, commercial, scientific, and defense applications in image processing often require transmission of large amounts of data across bandwidth-limited channels. Applications require robust transforms simultaneously minimizing bandwidth requirements and image resolution loss. Image processing algorithms take advantage of quantization to provide substantial lossy compression ratios at the expense of resolution. Recent research demonstrates that genetic algorithms evolve filters outperforming standard discrete wavelet transforms in conditions subject to high quantization error. While evolved filters improve overall image quality, wavelet filters typically provide a superior high frequency response, demonstrating improved reconstruction near the edges of objects within an image. This paper presents an algorithm to generate transform filters that optimize edge reconstruction, improving object edge resolution by an average of 17%. Edges within satellite images are isolated, and image transforms are evolved to optimize both the edge and non-edge portions of reconnaissance photographs. Such filters provide an increased object resolution over standard wavelets and traditionally evolved filters for varied applications of image processing.