Homomorphic Enhancement of Infrared Images Using the Additive Wavelet Transform

Abstract

Abstract-This paper presents a new enhancement technique for infrared images. This technique combines the benefits of homomorphic image processing and the additive wavelet transform. The idea behind this technique is based on decomposing the image into subbands in an additive fashion using the additive wavelet transform. This transform gives the image as an addition of subbands of the same resolution. The homomorphic processing is performed on each subband, separately. It is known that the homomorphic processing on images is performed in the log domain which transforms the image into illumination and reflectance components. Enhancement of the reflectance reinforces details in the image. So, applying this process in each subband enhances the details of the image in each subband. Finally, an inverse additive wavelet transform is performed on the homomorphic enhanced subbands to get an infrared image with better visual details.

Morphology-Based Homomorphic Filter for Contrast Enhancement of Mammographic Images

“…The homomorphic filtering techniques have shown that they are capable of compressing the dynamic range and enhancing the contrast simultaneously [2,3]. They concentrate on reinforcing the details of the image to be enhanced in terms of the frequency domain.…”

“…The conventional mapping gain used in the wavelet sub-bands for the noise suppression and the contrast enhancement is experimentally determined [2,3]. Since a feature of image object is very difficult to express mathematically, finding an optimal mapping gain for the feature extraction and a structuring element for the morphological filter is difficult and time-consuming task by trial and error.…”

Evolutionary Design of Morphology-Based Homomorphic Filter for Feature Enhancement of Medical Images

“…The conventional mapping gain used in the wavelet sub-bands for the noise suppression and the contrast enhancement is experimentally determined [1]- [3]. Since a feature of image object is very difficult to express mathematically, finding an optimal mapping gain for the feature extraction and a structuring element for the morphological filter is difficult and time-consuming task by trial and error.…”

“…The wavelet and homomorphic filtering techniques have shown that they are capable of compressing the dynamic range and enhancing the contrast simultaneously [1]- [3]. They concentrate on reinforcing the details of the image to be enhanced in terms of the frequency domain.…”

“…Based on the well established concepts of wavelet approach [1] and the wavelet-based homomorphic-based filtering methods [2], [3], the proposed method decomposes an image into morphological sub-bands, and then performs the homomorphic filtering using the morphological sub-bands. The evolutionary design of an optimal mapping gain and a structuring element for each morphological sub-band is carried out by DE scheme with the fast convergence.…”