The effect of Laplacian filter in adaptive unsharp masking for infrared image enhancement

Image processing, in particular image enhancement techniques have been the focal point of considerable research activity in the last decade. With the aid of an existing image enhancement technique, adaptive unsharp masking (AUM), we propose a novel kernel to be used in AUM filtering in order to enhance discontinuities which occur on the edges of targets of interest in infrared (IR) images. The proposed method uses an adaptive filter approach where an objective function is minimized by using descent algorithms. The output IR image has better sharpness and contrast adjustment for the detection of targets in terms of objective quality metrics. Hence, the proposed method ensures that the edges of the targets in IR images are sharper and that the quality of contrast adjustment has its optimum level in terms of peak signal-to-noise ratios.

► AUM ensures that the edges of the targets in IR images are sharper than that of CLUM. ► The quality of contrast adjustment has its optimum level in terms of PSNR with minimum error in AUM. ► It can be noted that τ1 and τ2 are dominant parameters amongst the others in AUM. ► Proposed criteria for optimum filter and parameter selection are applicable to any image.