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The problem of image restoration has a long and well-travelled history. Image restoration is still a valid challenge. The two main limitations in image accuracy are noise and blur. Image restoration includes removing noise from the image and removing the blur from the image. This paper proposes a unified framework for performing image denoising and deblurring. The restoration task is performed progressively and the task of restoration executed in a repeated manner. The number of repetition is based on the noise or blur level, and then the task of restoration is performed. In this way it recovers more and more image details and edges. We test our algorithm based on psnr value and it shows a higher performance than state-of-the-art algorithms.
The problem of haze removal has a long and well-travelled history. Dehazing from a single input hazy image is very challenging. This is because we have a little knowledge about the image. Since concentration of the haze in a hazy image is different from place to place it will become very hard to detect haze in image. The proposed work is a fast single image haze removal technique and it is work by using color attenuation prior. This method remove haze progressively. A linear model is created and the depth map is produced by using that model. From depth map estimate the transmission diagram and restore the scene radiance. Thus haze can be removed efficiently. Experimental results show that color attenuation prior model will work more efficiently than state-of-art haze removal algorithms.