Detection of Stomach Cancer by TV-Endoscope Colour Enhancement Image Processing

Using modern TV-endoscope equipment, we have experimented image processing in several ways. This time, we designed new image processing unit called “Color Subtracted Enhancement image processing unit”. This unit　was made by combining the RGB-Subtraction　image processing device with the color enhancement image processing device. The　RGB-subtraction device is an analogue unit that　enables us to remove the visible red, green and blue peaks from the image. We can subtract the images R-G, R-B, G-R, B-R, B-G simply by changing the switches. The color enhancement device (developed by Olympus optical corporation inc.) is a digital processing unit that lets us reinforce any of these colors. This unit let us enhance any of RGB colors on the image which is processed by RGB-subtraction device. 
 Using this unit, we tried to clarify the border between normal and diseased mucosa. We detected invasion of the lesion and compared　the original image with the processed images. Moreover, we marked the border visualized by this unit and compared the marking point with histological border .
Result: Based on the R-G image (i.e. that was produced by subtracting green from red) and the R-B image, the processed images are more clearly than those based on the B-G image. The G-B image, the B-R image and the G-R image. The color subtracted enhancement image ocessing enabled us to detect the border between normal and diseased mucosa and showed us the concavity and convexity of early cancer and gastric ulcers.
Conclusions: Elevations, depressions and color changes of gastric mucosa were clearly 
visible by using the TV-endoscope color subtracted enhancement image processing unit. This technique will facilitate the treatment of gastric lesions with endoscopic surgery.

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Aesthetic-Driven Image Enhancement by Adversarial Learning

We introduce EnhanceGAN, an adversarial learning based model that performs automatic image enhancement. Traditional image enhancement frameworks typically involve training models in a fully-supervised manner, which require expensive annotations in the form of aligned image pairs. In contrast to these approaches, our proposed EnhanceGAN only requires weak supervision (binary labels on image aesthetic quality) and is able to learn enhancement operators for the task of aesthetic-based image enhancement. In particular, we show the effectiveness of a piecewise color enhancement module trained with weak supervision, and extend the proposed EnhanceGAN framework to learning a deep filtering-based aesthetic enhancer. The full differentiability of our image enhancement operators enables the training of EnhanceGAN in an end-to-end manner. We further demonstrate the capability of EnhanceGAN in learning aesthetic-based image cropping without any groundtruth cropping pairs. Our weakly-supervised EnhanceGAN reports competitive quantitative results on aesthetic-based color enhancement as well as automatic image cropping, and a user study confirms that our image enhancement results are on par with or even preferred over professional enhancement

Impact Of Image Enhancement Parameters On Variations In GM &CM

In this paper, impact of color image enhancement parameters on variations in global mean and contrast enhancement index is studied. For color image enhancement human visual system based adaptive filter is used. This algorithm considers color information for enhancing image. For this parameters are used for enhancement process. This paper reveals how these parameters affect enhancement results. Experimental results show that these parameters affect mean and contrast enhancement index

Design of Novel Algorithm and Architecture for Gaussian Based Color Image Enhancement System for Real Time Applications

This paper presents the development of a new algorithm for Gaussian based color image enhancement system. The algorithm has been designed into architecture suitable for FPGA/ASIC implementation. The color image enhancement is achieved by first convolving an original image with a Gaussian kernel since Gaussian distribution is a point spread function which smoothen the image. Further, logarithm-domain processing and gain/offset corrections are employed in order to enhance and translate pixels into the display range of 0 to 255. The proposed algorithm not only provides better dynamic range compression and color rendition effect but also achieves color constancy in an image. The design exploits high degrees of pipelining and parallel processing to achieve real time performance. The design has been realized by RTL compliant Verilog coding and fits into a single FPGA with a gate count utilization of 321,804. The proposed method is implemented using Xilinx Virtex-II Pro XC2VP40-7FF1148 FPGA device and is capable of processing high resolution color motion pictures of sizes of up to 1600x1200 pixels at the real time video rate of 116 frames per second. This shows that the proposed design would work for not only still images but also for high resolution video sequences.Comment: 15 pages, 15 figure