In this paper, we make contact with the field of nonparametric statistics and present a development and generalization of tools and results for use in image processing and reconstruction. In particular, we adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more. Furthermore, we establish key relationships with some popular existing methods and show how several of these algorithms, including the recently popularized bilateral filter, are special cases of the proposed framework. The resulting algorithms and analyses are amply illustrated with practical examples

Kernel Regression for Image Processing and Reconstruction

Histogram equalization is a simple and effective image enhancing technique. But in some conditions, the luminance of an image may be changed significantly after the equalizing process, this is why it has never been utilized in a video system in the past. A novel histogram equalization technique, equal area dualistic sub-image histogram equalization, is put forward in this paper. First, the image is decomposed into two equal area sub-images based on its original probability density function. Then the two sub-images are equalized respectively. Finally, we obtain the results after the processed sub-images are composed into one image. The simulation results indicate that the algorithm can not only enhance the image information effectively but also preserve the original image luminance well enough to make it possible to be used in a video system directly.

Image enhancement based on equal area dualistic sub-image histogram equalization method

In this paper, a smart contrast enhancement technique based on conventional histogram equalization (HE) algorithm is proposed. This dynamic histogram equalization (DHE) technique takes control over the effect of traditional HE so that it performs the enhancement of an image without making any loss of details in it. DHE partitions the image histogram based on local minima and assigns specific gray level ranges for each partition before equalizing them separately. These partitions further go though a repartitioning test to ensure the absence of any dominating portions. This method outperforms other present approaches by enhancing the contrast well without introducing severe side effects, such as washed out appearance, checkerboard effects etc., or undesirable artifacts.

A Dynamic Histogram Equalization for Image Contrast Enhancement

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extends. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a novel extension of BBHE referred to as minimum mean brightness error bi-histogram equalization (MMBEBHE) to provide maximum brightness preservation. BBHE separates the input image's histogram into two based on input mean before equalizing them independently. This paper proposes to perform the separation based on the threshold level, which would yield minimum absolute mean brightness error (AMBE - the absolute difference between input and output mean). An efficient recursive integer-based computation for AMBE has been formulated to facilitate real time implementation. Simulation results using sample image which represent images with very low, very high and medium mean brightness show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), can be properly enhanced by MMBEBHE. Besides, MMBEBHE also demonstrate comparable performance with BBHE and DSIHE when come to use the sample images show in [Yeong-Taeg Kim, February 1997] and [Yu Wan et al., October 5 1999].

/pdf/minimum-mean-brightness-error-bi-histogram-equalization-in-2rwcg6iunl.pdf

Minimum mean brightness error bi-histogram equalization in contrast enhancement

Histogram equalization (HE) is widely used for contrast enhancement. However, it tends to change the brightness of an image and hence, not suitable for consumer electronic products, where preserving the original brightness is essential to avoid annoying artifacts. Bi-histogram equalization (BBHE) has been proposed and analyzed mathematically that it can preserve the original brightness to a certain extend. However, there are still cases that are not handled well by BBHE, as they require higher degree of preservation. This paper proposes a generalization of BBHE referred to as recursive mean-separate histogram equalization (RMSHE) to provide not only better but also scalable brightness preservation. BBHE separates the input image's histogram into two based on its mean before equalizing them independently. While the separation is done only once in BBHE, this paper proposes to perform the separation recursively; separate each new histogram further based on their respective mean. It is analyzed mathematically that the output image's mean brightness will converge to the input image's mean brightness as the number of recursive mean separation increases. Besides, the recursive nature of RMSHE also allows scalable brightness preservation, which is very useful in consumer electronics. Simulation results show that the cases which are not handled well by HE, BBHE and dualistic sub image histogram equalization (DSIHE), have been properly enhanced by RMSHE.

/pdf/contrast-enhancement-using-recursive-mean-separate-histogram-51e3610au0.pdf

Contrast enhancement using recursive mean-separate histogram equalization for scalable brightness preservation

Histogram equalization is widely used for contrast enhancement in a variety of applications due to its simple function and effectiveness. Examples include medical image processing and radar signal processing. One drawback of the histogram equalization can be found on the fact that the brightness of an image can be changed after the histogram equalization, which is mainly due to the flattening property of the histogram equalization. Thus, it is rarely utilized in consumer electronic products such as TV where preserving the original input brightness may be necessary in order not to introduce unnecessary visual deterioration. This paper proposes a novel extension of histogram equalization to overcome such a drawback of histogram equalization. The essence of the proposed algorithm is to utilize independent histogram equalizations separately over two subimages obtained by decomposing the input image based on its mean with a constraint that the resulting equalized subimages are bounded by each other around the input mean. It is shown mathematically that the proposed algorithm preserves the mean brightness of a given image significantly well compared to typical histogram equalization while enhancing the contrast and, thus, provides a natural enhancement that can be utilized in consumer electronic products.

Contrast enhancement using brightness preserving bi-histogram equalization

A digital system is provided that combines a digital video camera with television displays, and controlled by a controller module to enhance television performance according to personal television settings, parental controls and energy saving functions, achieved by utilizing the camera and face detection/recognition methods implemented in the controller module.

Personal settings, parental control, and energy saving control of television with digital video camera

A display system that displays a mixture of 2D and 3D video content is provided. A switching mechanism in the display system turns the 2D/3D mode of different portions of the screen on and off independently and automatically. Any type (2D, 3D, mixed) of video content can be displayed on any part of the screen simultaneously. The switching between different modes (2D, 3D, mixed) is performed automatically according to input control signals received from an internal device, or determined by a 2D/3D content detector in a computer, set-top-box, and/or cable/satellite tuner/receiver.

3D displays with flexible switching capability of 2D/3D viewing modes

In an image enhancement method and a circuit therefor, a luminance signal is extracted from input color signals. The luminance signal, extracted in screen units, is divided into subimages according to the mean value of the luminance signal. The gray level histograms of the divided subimages are independently equalized, and an adjusted luminance signal is output. The color signals are varied on the basis of the adjusted luminance signal, and compensated color signals are output. Accordingly, an abrupt variation in brightness and an artifact are effectively reduced, image contrast is enhanced, and an undistorted color signal is provided at the same time. Also, in order to reduce the hardware of the circuit, an input luminance image is quantized, the quantized image is divided into a predetermined number of quantized subimages on the basis of the mean of the quantized image, and an independent histogram equalization is performed on each of the quantized subimages.

Image enhancing method and circuit using mean separate/quantized mean separate histogram equalization and color compensation

According to an image quality enhancing circuit and a method therefor, an impulse for each of an input luminance and color signals is detected. A trimmed mean of a predetermined-sized window is output when the impulse is detected. Otherwise, the input signals are bypassed. Accordingly, noise-reduced luminance and color signals are output. An enhanced luminance signal is output by receiving the noise-reduced luminance signal in a picture unit and independently equalizing histograms for subimages divided on the basis of the mean value of the received luminance signal. A local contrast defined as a difference between the value of an input sample with respect to the enhanced luminance signal and each value obtained by low-pass filtering the samples in a predetermined-sized window including the input sample, and the input sample value is adaptively weighted according to the detected local contrast, so that a changed luminance signal is output. A compensated color signal is output by compensating the noise-reduced color signal according to the changed luminance signal. Thus, impulse noise is effectively removed, and an undistorted color signal is provided while enhancing the contrast of the luminance signal, so that the image quality is greatly improved.

Yeong-Taeg Kim

Papers

Contrast enhancement using brightness preserving bi-histogram equalization

Personal settings, parental control, and energy saving control of television with digital video camera

3D displays with flexible switching capability of 2D/3D viewing modes

Image enhancing method and circuit using mean separate/quantized mean separate histogram equalization and color compensation

Image quality enhancement circuit and method therefor