Abstract-In many practical cases of image processing, only a noisy image is available. Many image denoising methods usually require the exact value of the noise distribution as an essential filter parameter. However, to estimate the noise solely from the information of the noisy image is a difficult task. A simple but accurate noise estimator would significantly benefit many image denoising methods. In this paper, we present a method to estimate additive noise by utilizing the mean deviation of a smooth region selected from a noisy image. The noise distribution is estimated by computing the average mean deviation of all non-overlapping blocks in the smooth region. Simulation results demonstrate that our method achieves accurate noise estimation regardless of the image characteristics over a range of noise levels. The restoration performance of a denoising technique based on our noise estimation method resembles that achieved in the ideal condition.

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The food intake counting method showed a good significance that can lead to a successful weight loss by simply monitoring the food intake taken during eating. The device used in this project was Kinect Xbox One which used a depth camera to detect the motion of a person’s gesture and posture during food intake. Previous studies have shown that most of the methods used to count food intake device is worn device type. The recent trend is now going towards non-wearable devices due to the difficulty when wearing devices and it has high false alarm ratio. The proposed system gets data from the Kinect camera and monitors the gesture of the user while eating. Then, the gesture data is collected to be recognized and it will start counting the food intake taken by the user. The system recognizes the patterns of the food intake from the user by following the algorithm to analyze the gesture of the basic eating type and the system get an average accuracy of 96.2%. This system can help people who are trying to follow a proper way to avoid being overweight or having eating disorders by monitoring their meal intake and controlling their eating rate.

With increasing demand on digital images, there is a need to compress the image to entertain the limited bandwidth and storage capacity. Recently, there is a growing interest among researchers focusing on compression of various types of images and data. Amongst various compression algorithms, transform-based compression is one of the promising algorithms. Despite the technological advances in transmission and storage, the demands placed on the bandwidth of communication and storage capacities by far outstrips its availability. This paper presents a review of image compression principle, compression techniques and various thresholding algorithms (pre-processing algorithms) and quantization algorithm (post-processing algorithms). This paper intends to give an overview to the relevant parties to choose the suitable image compression algorithms to suit with the need.

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