<span id="docs-internal-guid-a16efc88-7fff-5adf-531b-900845049730"><span>More recent digital camera introduced enormous facilities for users from different specifications to take images easily, but the user still wants to improve these images, which it contains different problems like ambiguous and colors is not clear, because not enough light, cloudy weather, bright light, dark region and it's taken from remote distances. This paper aims to use a new approach for fusion images by using a wavelet coefficient based on PSNR and SNR measure (the technical result) instead of using the max, min, average values, and so on in the previous methods. The wavelet coefficient of each sub band is compared between them, the sub band had a value higher of measure is selected for fusion. Firstly, a discrete wavelet transform has been applied to the medical images with 2level. Then, the peak signal to noise ratio and signal to noise ratio measures have been computed for each sub-band. After that PSNR and SNR values have been compared for each sub-band to opposite sub-band and it selected the better value of measures. Secondly, PSNR and SNR values have been gathered for each image. Then select the image that contains value higher PSNR and lower value of SNR for purpose fusion. Finally, perform an inverse discrete wavelet on the fused image to transform it from the frequency to the spatial domain. The results of the work showed that the wavelet coefficient is used to preserve the image details and removed the noise. PSNR value of 1level of dwt is higher than 2level. This paper makes the image more clearer and informative than the original images. </span></span>

The development of complex life leads into a need using images in several fields, because these images degraded during capturing the image from mobiles, cameras and persons who do not have sufficient experience in capturing images. It was important using techniques differently to improve images and human perception as image enhancement and image restoration etc. In this paper, restoration noisy blurred images by guided filter and inverse filtering can be used for enhancing images from different types of degradation was proposed. In the color images denoising process, it was very significant for improving the edge and texture information. Eliminating noise can be enhanced by the image quality. In this article, at first, The color images were taken. Then, random noise and blur were added to the images. Then, the noisy blurred image passed to the guided filtering to get on denoised image. Finally, an inverse filter applied to the blurred image by convolution an image with a mask and getting on the enhanced image. The results of this research illustrated good outcomes compared with other methods for removing noise and blur based on PSNR measure. Also, it enhanced the image and retained the edge details in the denoising process. PSNR and SSIM measures were more sensitive to Gaussian noise than blur.

These days, We know that mobile phones with camera are very common to  used by most of the people because of their ease of use and carry . So I figured I need for mobile phone images processing. These images are often subjected to a variety of types of distortion, such as noise, poor illumination, and blur due to transmission , image acquisition and mobile movement during capturing scene. This Degradation leads to produce distorted images are not suitable for viewing or application. To overcome this degradation, this paper presents image improvement in mobile phone by distortion removal using Band Reject Filter and DFT to obtain on the best clear image. Periodic noise is sinusoidal of several of certain frequencies and periodic in nature. It looks identical to the bars that the image is covered. The proposed method is subjected to eliminate this noise from a set of images using Band Reject Filter using discrete Fourier transform that attenuate and replace a number of degradation bands. Then, computes the peak signal to noise ratio metric for each band. After that, select the largest value of each band for the purpose the making one clear image. Finally, the bands that carry value large are merged to produce a one image. The final image is converted to the spatial domain using an inverse Fourier transform. The experimental outcomes indicate that the proposed method improves the degraded image by a greater percentage without using the better value for metric and makes image more clear and  informative compare with the input image.