Automatic Design of Locally Adaptive Filters for Pre-processing of Images Subject to Further Interpretation

Abstract

Locally adaptive filters are widely used in image processing applications. However, their design commonly requires sufficient efforts and does not take into consideration some important aspects of further processing (interpreting and/or classification) of images. This paper puts forward a novel approach to automatic design of locally adaptive filters subject to further interpretation, namely, detection and localization of small size objects. Design is based on learning with clustering for a test image corrupted by a noise with statistical characteristics observed in real life images to which the obtained filter intend to be further applied. Quantitative data confirming the designed filter efficiency are presented.

Efficiency analysis of color image filtering

“…The map in Figure 8d shows that distortions introduced because of filtering are large for sharp edge and detail neighborhoods (shown as white in the binary map in Figure 8d). This conclusion once more stresses the known drawback of the DCT-based filter to introduce distortions in places of sharp transitions in images [25,62]. It means that if visual quality of filtered images is of prime importance, the attention in filter design should be paid to the preservation of edges and fine details in the first order.…”

“…We have also calculated R kSp for data arrays r k (n) (Figure 4b) and PSNR k (n) at filter outputs, n = 1,...25 for noise variance equal to 25. The values R kSp fall to the same range.…”

“…The third reason is that more accurate and adequate models of noise have been designed and new practical situations for which the already designed filters perform poorly have been found [18][19][20][21]. Next, for many applications there is a need to carry out image processing in automatic (fully blind), robust, adaptive, intelligent way, better suited for solving any final task [22][23][24][25]. This is especially crucial when there is a need to process a large number of images, e.g., multichannel images and/or remote data on-board.…”