'**SUMMARY**
**PARAMETERS**
**RETURNS**
**EXAMPLE**
NOTES:
SEE ALSO:'

def _inverseDFT(self, input):

    w = input[0].width
    h = input[0].height
    if (len(input) == 1):
        cv.DFT(input[0], input[0], cv.CV_DXT_INV_SCALE)
        result = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
        data = cv.CreateImage((w, h), cv.IPL_DEPTH_64F, 1)
        blank = cv.CreateImage((w, h), cv.IPL_DEPTH_64F, 1)
        cv.Split(input[0], data, blank, None, None)
        (min, max, pt1, pt2) = cv.MinMaxLoc(data)
        denom = (max - min)
        if (denom == 0):
            denom = 1
        cv.Scale(data, data, (1.0 / denom), ((1.0 * (- min)) / denom))
        cv.Mul(data, data, data, 255.0)
        cv.Convert(data, result)
        retVal = Image(result)
    else:
        results = []
        data = cv.CreateImage((w, h), cv.IPL_DEPTH_64F, 1)
        blank = cv.CreateImage((w, h), cv.IPL_DEPTH_64F, 1)
        for i in range(0, len(input)):
            cv.DFT(input[i], input[i], cv.CV_DXT_INV_SCALE)
            result = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 1)
            cv.Split(input[i], data, blank, None, None)
            (min, max, pt1, pt2) = cv.MinMaxLoc(data)
            denom = (max - min)
            if (denom == 0):
                denom = 1
            cv.Scale(data, data, (1.0 / denom), ((1.0 * (- min)) / denom))
            cv.Mul(data, data, data, 255.0)
            cv.Convert(data, result)
            results.append(result)
        retVal = cv.CreateImage((w, h), cv.IPL_DEPTH_8U, 3)
        cv.Merge(results[0], results[1], results[2], None, retVal)
        retVal = Image(retVal)
    del input
    return retVal
