The following figures show the results by applying homomorphic filter, high-pass filter, and both homomorphic and high-pass filter . All figures are produced by using Matlab. According to the figures one to four, we can see that how homomorphic filtering is used for correcting non-uniform illumination in image, and the image become clearer than the original image. On the other hand, if we apply high pass filter to homomorphic filtered image, the edges of the images become sharper and the other areas become dimmer. This result is as similar as just doing high pass filter only to the original image. Anti-homomorphic filtering It has been suggested that many cameras already have an approximately logarithmic response function (or more generally, a response function which tends to compress dynamic range), and display media such as television displays, photographic print media, etc., has an approximately anti-logarithmic response, or an otherwise dynamic range expansive response. Thus homomorphic filtering happens accidentally (unintentionally) whenever we process pixel values f(q) on the true quantigraphic unit of light q. Therefore it has been proposed that another useful kind of filtering is anti-homomorphic filtering in which images f(q) are first dynamic-range expanded to recover the true light q, upon which linear filtering is performed, followed by dynamic range compression back into image space for display. Audio and speech analysis Homomorphic filtering is used in the log-spectral domain to separate filter effects from excitation effects, for example in the computation of the cepstrum as a sound representation; enhancements in the log spectral domain can improve sound intelligibility, for example in hearing aids. Surface electromyography signals (sEMG) Homomorphic filtering was used to removes the effect of the stochastic impulse trains, which originates the sEMG signal, from the power spectrum of sEMG signal itself. In this way, only information on motor unit action potentialn (MUAP) shape and amplitude were maintained, and then, used to estimate the parameters of a time-domain model of the MUAP itself. Neural decoding How individual neurons or networks encode information is the subject of numerous studies and research. In central nervous system it mainly happens by altering the spike firing rate (frequency encoding) or relative spike timing (time encoding). Time encoding consists of altering the random inter-spikes intervals (ISI) of the stochastic impulse train in output from a neuron. Homomorphic filtering was used in this latter case to obtain ISI variations from the power spectrum of the spike