히스토그램 평활화는 영상의 밝기 값 분포가 한 곳에 밀집되어 있는 경우, 출력영상의 밝기 값 범위가 지나치게 확장되어 시각적으로 부자연스러운 결과를 초래하는 단점을 가지고 있다. 그런데 감마변환은 이런 부자연스러운 현상을 비선형적 변환을 통해 보정해주는 성질을 가지고 있다. 따라서 본 논문에서는 감마변환의 이런 성질을 이용하여 영상의 화질을 개선하는 새로운 히스토그램 평활화 방법을 제안한다. 제안 방법은 먼저, 입력영상의 평균 밝기 값을 이용하여 적절한 감마변환 식을 도출하고, 입력영상의 CDF(Cumulative Distribution Function)를 도출된 감마변환 식과 선형 결합하여 새로운 CDF를 생성한 후, 새롭게 변형된 CDF를 사용하여 히스토그램 평활화를 수행한다. 실험결과 제안방법이 기존방법들에 비해 entropy, UIQ, SSIM 등과 같은 정량적 평가에서 좋은 성능을 보였고, 시각적 관점에서도 자연스럽게 화질을 개선하였다.
히스토그램 평활화는 영상의 밝기 값 분포가 한 곳에 밀집되어 있는 경우, 출력영상의 밝기 값 범위가 지나치게 확장되어 시각적으로 부자연스러운 결과를 초래하는 단점을 가지고 있다. 그런데 감마변환은 이런 부자연스러운 현상을 비선형적 변환을 통해 보정해주는 성질을 가지고 있다. 따라서 본 논문에서는 감마변환의 이런 성질을 이용하여 영상의 화질을 개선하는 새로운 히스토그램 평활화 방법을 제안한다. 제안 방법은 먼저, 입력영상의 평균 밝기 값을 이용하여 적절한 감마변환 식을 도출하고, 입력영상의 CDF(Cumulative Distribution Function)를 도출된 감마변환 식과 선형 결합하여 새로운 CDF를 생성한 후, 새롭게 변형된 CDF를 사용하여 히스토그램 평활화를 수행한다. 실험결과 제안방법이 기존방법들에 비해 entropy, UIQ, SSIM 등과 같은 정량적 평가에서 좋은 성능을 보였고, 시각적 관점에서도 자연스럽게 화질을 개선하였다.
Histogram equalization generally has the disadvantage that if the distribution of the gray level of an image is concentrated in one place, then the range of the gray level in the output image is excessively expanded, which then produces a visually unnatural result. However, a gamma transformation ca...
Histogram equalization generally has the disadvantage that if the distribution of the gray level of an image is concentrated in one place, then the range of the gray level in the output image is excessively expanded, which then produces a visually unnatural result. However, a gamma transformation can reduce such unnatural appearances since it operates under a nonlinear regime. Therefore, this paper proposes a new histogram equalization method that can improve image quality by using a gamma transformation. The proposed method 1) derives the proper form of the gamma transformation by using the average brightness of the input image, 2) linearly combines the earlier gamma transformation with a CDF (Cumulative Distribution Function) for the image in order to obtain a new CDF, and 3) to finally perform histogram equalization by using the new CDF. The experimental results show that relative to existing methods, the proposed method provides good performance in terms of quantitative measures, such as entropy, UIQ, SSIM, etc., and it also naturally enhances the image quality in visual perspective as well.
Histogram equalization generally has the disadvantage that if the distribution of the gray level of an image is concentrated in one place, then the range of the gray level in the output image is excessively expanded, which then produces a visually unnatural result. However, a gamma transformation can reduce such unnatural appearances since it operates under a nonlinear regime. Therefore, this paper proposes a new histogram equalization method that can improve image quality by using a gamma transformation. The proposed method 1) derives the proper form of the gamma transformation by using the average brightness of the input image, 2) linearly combines the earlier gamma transformation with a CDF (Cumulative Distribution Function) for the image in order to obtain a new CDF, and 3) to finally perform histogram equalization by using the new CDF. The experimental results show that relative to existing methods, the proposed method provides good performance in terms of quantitative measures, such as entropy, UIQ, SSIM, etc., and it also naturally enhances the image quality in visual perspective as well.
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